Why This Role Matters 

This role powers Tekion’s AI‑native, end‑to‑end automotive platform by turning unified dealership data across DMS, CRM, Digital Retail, Service, and Payments into real‑time intelligence. You’ll operationalize a graph‑based contextual ecosystem so agents can retrieve the right context, enforce policy, and personalize experiences that drive measurable dealer outcomes. You’ll also build the resilient control layer - MCP and the LLM Gateway - that enables safe, cost‑efficient, multi‑provider LLM usage. Finally, you’ll define the standards for building, evaluating, deploying, and governing agentic systems so product teams can ship AI features quickly, safely, and at scale. In addition to enabling agentic systems powered by LLMs, this role also drives building the platform for classical ML models driving optimization across dealership operations.  

What Makes This Opportunity Unique 

This role offers direct, measurable impact on dealer outcomes and consumer experiences across Tekion’s Automotive Retail Cloud and Automotive Enterprise Cloud, with end‑to‑end ownership of an LLM control plane and gateway that serve multi‑tenant workloads under SLAs and, quality and cost guardrails. You’ll leverage a rich vertical dataset and domain graph spanning sales, service, parts, F&I, accounting, and consumer touchpoints to power context‑aware agents and retrieval‑augmented generation. You’ll also shape core levers - agent orchestration patterns, evaluation frameworks, and safety guardrails - so improvements in latency, reliability, evaluation quality, and safety translate into dealer KPIs like upsell, cycle time, CSAT, and service revenue.  You’ll also maintain and enhance the platform to support classical supervised and unsupervised ML models . 

Responsibilities 

• Build and run the LLM control plane/gateway: smart routing, rate limits/quotas, failover, and token/cost tracking. 

• Ship a unified API and SDKs (REST/gRPC) with normalized schemas, structured outputs, caching, and full observability (traces/logs/metrics). 

• Enforce safety and privacy by default: content filtering, prompt/response validation, and PII redaction. 

• Enable multi‑model, multi‑vendor use LLMs with automated canarying and versioning. 

• Own the agent runtime: tool registry, permissions, function calling, grounding, and retrieval. 

• Design orchestration patterns (sequential, planner‑executor, streaming) and manage agent state and long‑running workflows. 

• Enabling platform components for training and scoring pipelines for classical ML (e.g., XGBoost/LightGBM/linear/trees) and deep models; standardize experiment tracking and packaging. 

• Create components to Monitor model and data drift, retraining and tuning models as needed to maintain accuracy and relevance. 

• Add human‑in‑the‑loop review and safe‑actioning before agents touch dealer systems. 

• Evolve the domain graph and entity resolution; build reliable data ingestion pipelines. 

• Serve real‑time context to agents (profiles, inventory, pricing, appointments, service history) with access controls and lineage. 

• Power retrieval with hybrid search (graph + vector + keyword) and smart cache/TTL to balance accuracy, latency, and cost. 

• Run continuous offline/online evaluations for quality, factuality, bias, and safety for the platform sanity. 

• Define SLOs for latency (p50/p95), uptime, and cost view capabilities; enable autoscaling and spend controls. 

• Maintain a model/agent registry, versioning, approvals, audit trails, and reproducibility; support compliances where needed. 

• Provide templates/CLIs, sandboxes, and docs so product teams can build and ship fast; mentor engineers and champion MLOps and AI safety best practices. 

Desired Skills & Experience 

• 12–15+ years building large‑scale data/ML or platform systems; strong software engineering fundamentals (Abstracted API design, concurrency, distributed systems). 

• Production experience with Python plus one of Java/Scala/Go; microservices and API design. 

• MLOps at scale: pipelines (Airflow/Kubeflow), tracking/registry (MLflow), CI/CD for models, A/B testing, shadow/canary, and online feature computation (Spark/Flink/Kafka). 

• Cloud and containers: AWS (preferred), plus Docker/Kubernetes; performance, reliability, and cost engineering in multi‑tenant SaaS. 

• Practical ML knowledge (feature engineering, training, evaluation, drift detection); experience deploying models that power user‑facing workflows. 

• Built or operated an LLM gateway/control plane: provider adapters, routing/policies, caching, quota/rate‑limit, cost and token accounting. 

• Agentic systems: tool use/function calling, orchestration frameworks, human‑in‑the‑loop, safety/guardrails, and online evaluation/telemetry. 

• Graph and retrieval: knowledge graphs (e.g., Neo4j/Neptune/TigerGraph), GraphQL, vector search (e.g., pgvector/Qdrant/Milvus), hybrid retrieval patterns. 

Preferred Mindset 

• Platform‑as‑product: obsess over developer experience, paved roads, and clear SLAs. 

• Thinks in systems - observability, fallback, access control are core, not afterthoughts. 

• Passionate about AI - enjoys enabling real-world LLM and agentic use cases. 

• Cost‑aware builder: you treat latency and dollars as first‑class metrics and design for graceful degradation. 

• Vendor‑agnostic thinker: choose the right model/provider per use case; build for portability and resilience. 

• Documentation and teaching: you make complex systems understandable; you uplevel teams. 

As a Senior Database Engineer at Tekion, you will design, automate, and optimise database operations across our cloud-native automotive retail platform, connecting OEMs, dealerships, partners, and consumers. You’ll be working on scaling problems, laying down processes and streamlining requirements to actual executable tasks.

You’re expected to know in-depth about different storage and replication internals, trade-offs across SQL/NoSQL technologies, distributed systems fundamentals, transaction interaction and concurrency. A combination of this with strong Python fundamentals and Python scripting experience would make you a strong candidate for this role.

You should have at least 4+ years of experience. You should know about MongoDB in depth, and at least one SQL database like MySQL, MariaDB, PostgreSQL. People who want to build systems, not just manage them.

Key Responsibilities:

1. Core Database Engineering & Distributed Systems (Primary Focus):
1. Debug and prevent concurrency issues (deadlocks, lock escalation, starvation, long-running transactions) and advise teams on safe patterns (idempotency, retries, backoff, fencing tokens).
2. B-tree/LSM trade-offs, page layout, WiredTiger cache, journaling, WAL/redo/undo, checkpoints, compaction, MVCC behaviour
3. Backups (Logical vs Physical vs Snapshots based), upgradation of database systems and versions, data migration, and Disaster Recovery implementation
4. Query planner behaviour, statistics, cardinality estimation pitfalls
5. Indexing strategies and write amplification trade-offs
2. Reliability Engineering, Observability & Incident Response :
   Build and implement database reliability practices:

1. Lead incident response and postmortems; implement preventative controls and automated remediation.
2. Implement monitoring and alerting for replication lag, lock contention, buffer/cache health, slow query patterns, storage growth, and failover events.

1. Python Automation & Platform Tooling (Required):
   Develop and maintain robust Python tooling/services for:

• Automated health checks, failover verification, backup/restore validation, and consistency checks.
• Online schema change orchestration, safe rollout/rollback workflows, and guardrails in automation.
• Performance diagnostics automation (plan capture, workload fingerprinting).
• Build scalable automation that handles millions of records/events efficiently (attention to time/space complexity).

1. Data Modelling Across SQL & NoSQL:
   Guide data modelling decisions across relational and document/Key-Value stores:

• Normalisation vs denormalisation trade-offs
• Secondary index design, hot partition mitigation, and throughput planning
• TTL, archiving, and lifecycle policies
• Define governance for data correctness, durability, and operational safety in high-throughput systems

1. Collaboration, Standards & Enablement

1. Partner with application teams to design safe data access patterns, migration strategies, and operational standards.
2. Define best practices for production change management, access controls, auditing, and compliance requirements.
3. Mentor engineers and raise the bar on database engineering craftsmanship across the org.
4.  

Qualifications & Skills

Must have

• 4+ years of work experience as an engineer in a relevant role and responsibilities.
• Strong foundation in distributed systems and database fundamentals: CAP, consensus basics, replication, failure handling, and consistency.
• Deep understanding of transaction semantics, isolation levels, MVCC, locking, concurrency failure modes, storage engines and DB internals.
• Strong Python skills for building production-grade automation and diagnostics tooling.
• Hands-on experience operating and tuning production databases (MongoDB and at least one SQL system such as MySQL, PostgreSQL, MariaDB etc).
• Strong debugging and performance analysis skills; comfortable working from metrics, logs, traces, and low-level symptoms.
• Experience working with Linux and familiarity with common commands.

Nice to Have

• Experience with multi-region data architectures, global tables, and cross-region failover strategies.
• Familiarity with AWS and MongoDB Atlas.
• Understanding of Networking concepts like VPC, subnets, security groups, CIDR, peering etc.
• Experience with Kubernetes and/or self-managed database experience
• Experience building internal platforms (self-serve database ops, policy-as-code, safe migration frameworks).

What We’re Looking For

A senior engineer who:

• Thinks in trade-offs (latency vs consistency, durability vs throughput, cost vs performance), thinks deeply.
• Can reason from first principles about database behaviour under failure and load.
• Uses Python to operationalise best practices into repeatable, safe automation and can instruct others on best practices.
• Drives reliability, performance, and correctness across both SQL and NoSQL systems.
• Leads through technical clarity, ownership, and mentorship.

Build and operate the production backbone that takes models from Applied Sciences (AS) and delivers reliable, low-latency ML services across Tekion’s DMS, CRM, Digital Retail, Service, Payments, and enterprise products. You’ll own pipelines, microservices, CI/CD, observability, and runtime reliability—working hand-in-hand with Applied Sciences and Product to turn ideas into measurable dealer and consumer impact. 

Why this Role Matters 

• Accelerate the rollout of LLM-powered and agent-driven features across Tekion products. 

• Enable agentic workflows that automate, reason, and interact on behalf of users and internal stakeholders. 

• Operationalize secure, compliant, and explainable LLM and agentic services at scale. 

• Convert Applied Sciences models into scalable, compliant, cost‑efficient production services. 

• Standardize how models are trained, validated, deployed, and monitored across Tekion products. 

• Power real-time, context-aware experiences by integrating batch/stream features, graph context, and online inference. 

What You’ll Do 

• Turn Applied Sciences prototype models (tabular, NLP/LLM, recommendation, forecasting) into fast, reliable services with well-defined API contracts. 

• Integrate with the LLM Gateway/MCP, prompt/config versioning. 

• Build and orchestrate CI/CD pipelines. 

• Review data science models; refactor and optimize code; containerize; deploy; version; and monitor for quality. 

• Collaborate with data scientists, data engineers, product managers, and architects to design enterprise systems. 

• Monitor, detect, and mitigate risks unique to LLMs and agentic systems. 

• Implement prompt management: versioning, A/B testing, guardrails, and dynamic orchestration based on feedback and metrics. 

• Design batch/stream pipelines (Airflow/Kubeflow, Spark/Flink, Kafka) and online features linked to our domain graph. 

• Build inference microservices (REST/gRPC) with schema versioning, structured outputs, and stringent p95 latency targets. 

• Manage the model/feature lifecycle: feature store strategy, model/agent registry, versioning, and lineage. 

• Instrument deep observability: traces/logs/metrics, data/feature drift, model performance, safety signals, and cost tracking. 

• Ensure real-time reliability: autoscaling, caching, circuit breakers, retries/fallbacks, and graceful degradation. 

• Develop templates/SDKs/CLIs, sandbox datasets, and documentation that make shipping ML the default path. 

Desired Skills and Experience 

• 6+ years in ML engineering/MLOps or backend/platform engineering with production ML. 

• Experience with LLMs, retrieval systems, vector stores, and graph/knowledge stores. 

• Strong software engineering fundamentals: Python plus one of Java/Go/Scala; API design; concurrency; testing. 

• Hands-on with orchestration frameworks and libraries (LangChain, LlamaIndex, OpenAI Function Calling, AgentKit, etc.). 

• Knowledge of agent architectures (reactive, planning, retrieval-augmented agents), and safe execution patterns. 

• Pipelines and data: Airflow/Kubeflow or similar; Spark/Flink; Kafka/Kinesis; strong data quality practices. 

• Microservices and runtime: Docker/Kubernetes, service meshes, REST/gRPC; performance and reliability engineering. 

• Model ops: experiment tracking, registries (e.g., MLflow), feature stores, A/B and shadow testing, drift detection. 

• Observability: OpenTelemetry/Prometheus/Grafana; debugging latency, tail behavior, and memory/CPU hotspots. 

• Cloud: AWS preferred (IAM, ECS/EKS, S3, RDS/DynamoDB, Step Functions/Lambda), with cost optimization experience. 

• Security/compliance: secrets management, RBAC/ABAC, PII handling, auditability. 

Preferred Mindset 

• Product-oriented: You measure success by dealer and consumer outcomes, not just technical metrics. 
• Reliability- and safety-first: You move fast with guardrails, rollbacks, and clear SLOs. 
• Systems thinker: You design for multi-tenant scale, portability, and cost efficiency
• Collaborative: You translate between Applied Sciences, Product, and the Data & AI Platform; you document and teach. 

Senior Software Engineer  -  Senior Machine Learning Platform

Why This Role Matters 

This role powers Tekion’s AI‑native, end‑to‑end automotive platform by turning unified dealership data across DMS, CRM, Digital Retail, Service, and Payments into real‑time intelligence. You’ll operationalize a graph‑based contextual ecosystem so agents can retrieve the right context, enforce policy, and personalize experiences that drive measurable dealer outcomes. You’ll also build the resilient control layer - MCP and the LLM Gateway - that enables safe, cost‑efficient, multi‑provider LLM usage. Finally, you’ll define the standards for building, evaluating, deploying, and governing agentic systems so product teams can ship AI features quickly, safely, and at scale. In addition to enabling agentic systems powered by LLMs, this role also drives building the platform for classical ML models driving optimization across dealership operations.  

What Makes This Opportunity Unique 

This role offers direct, measurable impact on dealer outcomes and consumer experiences across Tekion’s Automotive Retail Cloud and Automotive Enterprise Cloud, with end‑to‑end ownership of an LLM control plane and gateway that serve multi‑tenant workloads under SLAs and, quality and cost guardrails. You’ll leverage a rich vertical dataset and domain graph spanning sales, service, parts, F&I, accounting, and consumer touchpoints to power context‑aware agents and retrieval‑augmented generation. You’ll also shape core levers - agent orchestration patterns, evaluation frameworks, and safety guardrails - so improvements in latency, reliability, evaluation quality, and safety translate into dealer KPIs like upsell, cycle time, CSAT, and service revenue.  You’ll also maintain and enhance the platform to support classical supervised and unsupervised ML models . 

Responsibilities 

• Build and run the LLM control plane/gateway: smart routing, rate limits/quotas, failover, and token/cost tracking. 

• Ship a unified API and SDKs (REST/gRPC) with normalized schemas, structured outputs, caching, and full observability (traces/logs/metrics). 

• Enforce safety and privacy by default: content filtering, prompt/response validation, and PII redaction. 

• Enable multi‑model, multi‑vendor use LLMs with automated canarying and versioning. 

• Own the agent runtime: tool registry, permissions, function calling, grounding, and retrieval. 

• Design orchestration patterns (sequential, planner‑executor, streaming) and manage agent state and long‑running workflows. 

• Enabling platform components for training and scoring pipelines for classical ML (e.g., XGBoost/LightGBM/linear/trees) and deep models; standardize experiment tracking and packaging. 

• Create components to Monitor model and data drift, retraining and tuning models as needed to maintain accuracy and relevance. 

• Add human‑in‑the‑loop review and safe‑actioning before agents touch dealer systems. 

• Evolve the domain graph and entity resolution; build reliable data ingestion pipelines. 

• Serve real‑time context to agents (profiles, inventory, pricing, appointments, service history) with access controls and lineage. 

• Power retrieval with hybrid search (graph + vector + keyword) and smart cache/TTL to balance accuracy, latency, and cost. 

• Run continuous offline/online evaluations for quality, factuality, bias, and safety for the platform sanity. 

• Define SLOs for latency (p50/p95), uptime, and cost view capabilities; enable autoscaling and spend controls. 

• Maintain a model/agent registry, versioning, approvals, audit trails, and reproducibility; support compliances where needed. 

• Provide templates/CLIs, sandboxes, and docs so product teams can build and ship fast; mentor engineers and champion MLOps and AI safety best practices. 

Desired Skills & Experience 

• 5 - 7 years building large‑scale data/ML or platform systems; strong software engineering fundamentals (Abstracted API design, concurrency, distributed systems). 

• Production experience with Python plus one of Java/Scala/Go; microservices and API design. 

• MLOps at scale: pipelines (Airflow/Kubeflow), tracking/registry (MLflow), CI/CD for models, A/B testing, shadow/canary, and online feature computation (Spark/Flink/Kafka). 

• Cloud and containers: AWS (preferred), plus Docker/Kubernetes; performance, reliability, and cost engineering in multi‑tenant SaaS. 

• Practical ML knowledge (feature engineering, training, evaluation, drift detection); experience deploying models that power user‑facing workflows. 

• Built or operated an LLM gateway/control plane: provider adapters, routing/policies, caching, quota/rate‑limit, cost and token accounting. 

• Agentic systems: tool use/function calling, orchestration frameworks, human‑in‑the‑loop, safety/guardrails, and online evaluation/telemetry. 

• Graph and retrieval: knowledge graphs (e.g., Neo4j/Neptune/TigerGraph), GraphQL, vector search (e.g., pgvector/Qdrant/Milvus), hybrid retrieval patterns. 

Preferred Mindset 

• Platform‑as‑product: obsess over developer experience, paved roads, and clear SLAs. 

• Thinks in systems - observability, fallback, access control are core, not afterthoughts. 

• Passionate about AI - enjoys enabling real-world LLM and agentic use cases. 

• Cost‑aware builder: you treat latency and dollars as first‑class metrics and design for graceful degradation. 

• Vendor‑agnostic thinker: choose the right model/provider per use case; build for portability and resilience. 

• Documentation and teaching: you make complex systems understandable; you uplevel teams. 

Role Overview

As the Manager, Database Reliability Engineering (DBRE), you will lead a team of expert DBREs responsible for the health, performance, automation, and scalability of Tekion’s data backbone. Your focus will be on operational excellence, automation first, and continuous improvement of reliability and efficiency for our MongoDB, MySQL, and Elasticsearch clusters. You'll mentor your team, drive innovation in database automation (using Go or Python), and collaborate with product, engineering, and Cloud teams.

Key Responsibilities

• Team Leadership & Strategy: Build, mentor, and lead a high-performing Database Reliability Engineering team, fostering a culture of technical excellence, ownership, and collaboration.
• Database Operations & Reliability: Oversee the reliability, performance, availability, and scalability of MongoDB, MySQL, and Elasticsearch across multiple environments and regions.
• Automation & Engineering: Architect and drive automation for database provisioning, scaling, backups, upgrades, patching, and recovery using Go or Python. Establish standards for Infrastructure as Code and self-service database management.
• Monitoring & Incident Response: Implement and enhance automated monitoring, alerting, performance tuning, and self-healing solutions. Lead major incident response, postmortems, and continuous improvement.
• Collaboration: Partner with SRE, CloudOps, Security, and development teams to understand requirements, drive database reliability improvements, and support feature launches.
• Best Practices & Documentation: Define and enforce database reliability best practices, documentation, runbooks, and automation guides. Promote DBRE standards and knowledge sharing.
• Capacity & Cost Management: Monitor capacity, forecast growth, and work with engineering and product teams on cost-effective database scaling.
• Security & Compliance: Ensure security, compliance, and data protection in all database operations and automation solutions.

Required Qualifications

• Bachelor's or master’s in computer science, Engineering, or equivalent practical experience.
• 8+ years of experience in database engineering, DBRE, or SRE roles, with at least 3+ years in a technical/team leadership or management position.
• Deep hands-on experience managing and automating MongoDB, MySQL, and Elasticsearch (good to have).
• Strong programming skills in Go or Python for database automation and tooling.
• Proven ability to design and implement automation for database lifecycle operations, monitoring, and self-healing.
• Expertise in building, running, and scaling highly available database clusters (replication, sharding, backup/restore, performance tuning).
• Strong knowledge of database security, access controls, patching, and compliance practices.
• Excellent communication, incident leadership, and cross-functional collaboration skills.
• Track record of hiring, mentoring, and developing technical talent.
• Prior Manager experience is a must 

About Tekion 

Tekion is revolutionizing the automotive landscape through cloud-native platforms and intelligent products that power dealerships, OEMs, and partners worldwide. At Tekion, we’re passionate about building a future where technology simplifies, secures, and transforms the way vehicles are bought, sold, and serviced. Our collaborative and innovation-driven culture is fueled by a relentless commitment to security, reliability, and scalability as we accelerate our global footprint. 

Team Overview 

You will lead a high-impact Cloud Infrastructure Automation & SRE team within Cloud Engineering, focusing on building and operating secure, reliable, scalable, resilient & self-serve infrastructure platforms for all Tekion. In close partnership with our Cloud Security, Product Security, and Application teams, you’ll play a vital role in establishing and enforcing security best practices and automation-first principles across all layers of our cloud environments. 

Roles and Responsibilities 

Strategic Leadership: Lead and grow a team of talented cloud infrastructure engineers and SREs, providing technical and people leadership, mentorship, and career development. 

Infrastructure as Code: Drive the adoption and evolution of infrastructure automation (IaC) frameworks (such as Terraform, Pulumi, CRD) to enable consistent, auditable, and scalable platform management. 

Operational Excellence: Oversee the reliability, scalability, and performance of critical infrastructure services, ensuring high availability, rapid mitigation of incidents, and root cause analysis. 

Platform Enablement: Architect, build, and optimize cloud platforms (AWS/GCP/Azure), Kubernetes clusters, and CI/CD pipelines to support the needs of Tekion’s products and services. 

Security & Compliance: Partner with Cloud Security and Product Security teams to define, implement, and maintain robust security controls, policies, and monitoring across infrastructure and applications. 

Collaboration: Work cross-functionally with engineering, security, and application teams to balance operational needs, reliability, and security requirements in cloud environments. 

Incident Management: Establish, refine, and lead SRE practices for monitoring, alerting, on-call processes, incident response, and postmortems with a security-first approach. 

Automation & Tooling: Identify opportunities for automation across infrastructure, operations, and security workflows; enable self-service tooling and guardrails for developers. 

Continuous Improvement: Promote a culture of continuous learning, process optimization, and operational excellence, keeping up to date with emerging security and cloud technologies. 

Reporting & Metrics: Track and present key performance indicators (SLAs, SLOs, security posture, automation coverage, incident metrics) to stakeholders and executive leadership. 

What makes you relevant for this role? 

• Bachelor’s or master’s degree in computer science, Engineering, or related field OR equivalent practical experience. 

• 10+ years of experience in DevOps, Site Reliability Engineering, or Cloud Infrastructure roles, with at least 2+ years in technical leadership or management. 

• Hands-on expertise in cloud platforms (AWS/GCP/Azure), containerization (Docker, Kubernetes), and infrastructure-as-code (Terraform, CloudFormation, Ansible, etc.). 

• Deep knowledge of cloud security best practices, cloud-native IAM, network security, secrets management, and compliance (SOC2, ISO 27001, etc.). 

• Proven experience driving operational reliability with SRE methodologies: monitoring, alerting, incident response, and post-incident analysis. 

• Track record of building and developing high-performing infrastructure or SRE teams. 

• Strong background in automation, CI/CD pipeline management, and system architecture at scale. 

• Excellent communication, stakeholder management, and cross-functional collaboration skills. 

• Security Integration: Embed security best practices across the software development lifecycle (SDLC), from code commit to deployment, ensuring "security by design."
• Collaboration: Work closely with Cloud Security and Product Security teams to develop and enforce security standards, policies, and frameworks.
• Infrastructure Hardening: Implement and automate security controls across AWS/Azure/GCP resources, Kubernetes clusters, containers, and other infrastructure components.
• CI/CD Security: Integrate automated security testing, static and dynamic analysis, and vulnerability scanning into CI/CD pipelines.
• Threat Detection & Incident Response: Develop and maintain monitoring/alerting for security incidents; participate in incident triage, root cause analysis, and response efforts.
• Tooling & Automation: Build and maintain automation for compliance checks, secrets management, access controls, patching, and audit logging.
• Governance: Ensure the team’s infrastructure and deployments are compliant with industry regulations and Tekion’s internal security requirements.
• Continuous Improvement: Stay current with emerging threats, vulnerabilities, and security best practices; propose and implement relevant improvements.
• Evangelism: Foster a security-first mindset within DevOps and engineering teams through knowledge sharing, documentation, and training.

• Bachelor’s degree in computer science, Engineering, or a related technical field, or equivalent practical experience.
• 3-7 years of experience in DevOps, Site Reliability Engineering, or Security Engineering roles.
• Solid hands-on experience with cloud infrastructure (AWS/GCP/Azure).
• Deep understanding of secure infrastructure design, identity and access management, network security, and related best practices.
• Experience securing container platforms (Docker, Kubernetes) and modern CI/CD toolchains (e.g., Jenkin, Tekton, GitHub actions, ArgoCD).
• Proficiency in at least one scripting/programming language (Python, Go, etc.) and strong automation skills.
• Practical familiarity with vulnerability management, scanning, threat modeling, and remediation workflows.
• Strong collaboration, communication, and documentation abilities.

• Opportunity to work with some of the smartest minds to solve complex security challenges at scale.
• Impactful role in shaping and securing a global, cloud-native & AI driven platform.
• Innovative, collaborative, and fast-paced culture.

Summary:

The Senior Manager, DevOps leads the strategic direction and execution of Tekion’s DevOps and infrastructure automation initiatives. This role oversees global teams responsible for platform reliability, CI/CD, observability, and cloud operations. It focuses on scaling infrastructure, improving deployment velocity, and ensuring operational excellence across the engineering organization.

Duties & Responsibilities:

• Defines and executes the DevOps roadmap, aligning automation, reliability, and scalability goals with organizational priorities.
• Manages and scales teams responsible for cloud infrastructure, CI/CD pipelines, monitoring, and SRE practices.
• Partners with Engineering, Product, and Security leadership to align on architectural standards and operational policies.
• Leads modernization initiatives including infrastructure as code, containerization, and service mesh adoption.
• Establishes SLAs, incident response processes, and performance metrics for platform health and resilience.
• Oversees budget planning, vendor evaluation, and tooling strategy to ensure efficiency and scalability.
• Mentors DevOps managers and senior engineers, fostering a culture of accountability, innovation, and continuous improvement.

Job Complexity:

Leads global, cross-functional infrastructure programs with high strategic value. Balances short-term platform demands with long-term architectural and operational vision.

Required Supervision:

Operates with complete autonomy. Responsible for team leadership, budget oversight, and cross-department alignment with executive stakeholders.

Expertise:

Demonstrates deep expertise in DevOps, infrastructure automation, cloud architecture, and site reliability engineering. Influences enterprise-wide technical decisions and leads transformation across engineering operations.

Qualifications:

• 10+ years of experience in DevOps, cloud engineering, or infrastructure leadership roles
• 5+ years in technical leadership or management, including global team oversight
• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
• Strong expertise in AWS, GCP, or Azure and infrastructure-as-code tools like Terraform, CloudFormation, or Pulumi
• Proficiency in CI/CD pipeline tooling (e.g., Jenkins, GitLab CI/CD, ArgoCD) and observability stacks (e.g., Prometheus, Grafana, ELK)
• Experience implementing SRE practices, reliability metrics, and service-level objectives
• Excellent leadership, strategic thinking, and cross-functional collaboration skills

Tekion seeks an exceptionally talented and seasoned Staff / Lead Applied Scientist to architect the future of our AI-driven solutions. The incumbent will assume a pivotal role in our organization, providing the strategic and technical stewardship necessary to translate cutting-edge research into tangible business value. The ideal candidate is a first-principles thinker, adept at navigating ambiguity, and possesses the intellectual curiosity to formulate and solve complex, unstructured problems. This is seminal opportunity to influence the trajectory of our AI strategy and deliver transformative solutions at an enterprise scale.  
 
Key Responsibilities 

• Strategic Direction: Architect and execute the strategic vision for applied AI and Machine Learning within Tekion, ensuring rigorous alignment with overarching business objectives and industry challenges. 

• Innovation & Research Leadership: Steer the R&D roadmap by identifying and capitalizing on emergent technological opportunities. Drive innovation through the ideation and implementation of novel, scientifically-grounded solutions. 

• Stakeholder Alignment: Serve as the principal technical liaison between the R&D organization and executive stakeholders, ensuring technical strategies are congruent with business imperatives. 

• Cross-Functional Execution: Lead collaborations with product, engineering, and business units to define, develop, and integrate sophisticated machine learning systems into core products and services. 

• Scientific Contribution: Contribute to the broader scientific community through publications in premier academic conferences and journals, enhancing Tekion's position as a center of innovation. 

• Mentorship & Talent Development: Mentor and cultivate a culture of scientific rigor and innovation among applied scientists, guiding project execution and fostering professional growth. 

• Impact Quantification: Establish robust frameworks for model evaluation and performance measurement. Quantify and articulate the business impact of machine learning initiatives to executive leadership. 

• Governance & Best Practices: Institute and govern best practices for the entire machine learning lifecycle, including model development, validation, deployment, and monitoring, while championing ethical and responsible AI 

Core Competencies & Technical Expertise 

            The successful candidate will demonstrate mastery in the following areas: 

• Foundational Expertise: Deep, theoretical and practical expertise in Machine Learning, Deep Learning, Causal Inference, and Explainable AI. 

• Statistical Rigor: Advanced proficiency in applied probability and statistics to derive and validate insights from complex, high-dimensional data. 
   
   

Deep Learning: 

• Expert-level proficiency with frameworks such as TensorFlow, Keras, and PyTorch. 

• Extensive experience implementing advanced neural network architectures. 

• Practical application of Computer Vision (e.g., OpenCV) and Natural Language Processing (e.g.,spaCy) methodologies. 

• Large Language Models (LLMs): Demonstrated experience with Large Language Models, including advanced prompt engineering, fine-tuning, and deployment for specific business applications. 

Technical Proficiencies: 

• Advanced programming skills in Python and mastery of SQL. Familiarity with distributed computing frameworks (e.g., Spark) is advantageous. 

• Proficiency with cloud computing platforms (GCP, Azure, AWS). 

• Expertise in experimental design (A/B testing, causal inference). 

• Proficient in version control systems (Git). 

Required Qualifications 

• An advanced degree (Ph.D. or M.S. preferred) in a quantitative discipline such as Computer Science, Statistics, Operations Research, Physics, or a related field. 

• A minimum of 7 - 9 years of post-academic experience in applied science or quantitative research roles, with a demonstrable history of translating complex models into tangible business value. 

• A distinguished track record of solving difficult, business-critical problems using a scientific, data-driven approach. 

• Proven experience in programming, data manipulation, and building models in a business context. 

• Sophisticated data visualization and presentation skills, with the ability to distill complex analytical results into clear, actionable insights for diverse audiences. 
   

Ideal Candidate Profile 
 

• A profound intellectual curiosity and a systematic, first-principles approach to problem-solving. 

• Domain expertise in the automotive industry or related fields such as finance or enterprise SaaS is highly desirable. 

• Experience in specialized areas such as Recommendation Engines or advanced NLP is preferred. 

• Exceptional communication and presentation capabilities, with a proven ability to distill complex technical concepts for executive-level audiences. 

• A demonstrated ability to work collaboratively and effectively lead initiatives across multi-disciplinary teams. 

• A commitment to continuous personal and team development within a collaborative, high-performance culture. 

Perks and Benefits

• Competitive compensation 

• Generous stock options 

• Medical Insurance coverage 

• Work with some of the brightest minds from Silicon Valley’s most dominant and successful companies 

Why This Role Matters
We're looking for a Data Platform Engineer with deep systems engineering expertise and a passion for building reusable infrastructure. You'll help
architect and deliver the internal frameworks that power data ingestion, transformation, observability, and self-serve analytics across the company. Your work will be central to enabling scalable, multi-tenant ingestion pipelines (including CDC via Debezium), serving layers built on Trino, and robust tooling that lets teams move fast without compromising on data quality or platform
health.

What Makes This Opportunity Unique
Build platform tooling for multi-region, multi-tenant data infrastructure using modern open-source and cloud-native technologies.
Lead engineering efforts around CDC pipelines using Debezium, enabling reliable change capture from application databases.
Shape the developer experience for data engineers, from ingestion to publishing and consumption, with metadata-aware automation.
Drive the evolution of data serving infrastructure using Trino and open table formats like Delta Lake or Iceberg.
Work at the intersection of data engineering, infrastructure, and platform strategy, directly influencing core architectural decisions.

Responsibilities

• Design and implement foundational frameworks for ingestion, orchestration, schema validation, and metadata management.
• Build robust, scalable pipelines for Change Data Capture (CDC) using Debezium integrated with Kafka and Spark.
• Develop internal tooling to standardize and accelerate data ingestion, transformation, and publishing workflows.
• Optimize data serving layers powered by Trino, including metadata syncing, security filtering, and performance tuning.
• Partner with SRE and Infra teams to build autoscaling, self-healing, and cost-optimized Spark jobs on AWS EMR.
• Implement observability features — logs, metrics, alerts — for critical platform services and data pipelines.
• Define and enforce standards for schema evolution, lineage tracking, and data governance.
• Automate platform operations using CI/CD pipelines, metadata-driven configurations, and infrastructure-as-code.

Desired Skills & Experience

• 7+–12 years of experience in data platform, infrastructure, or backend systems engineering roles.
• Strong systems engineering fundamentals — distributed systems, fault tolerance, performance tuning.
• Expertise in Apache Spark with production experience on AWS EMR.
• Experience building or managing CDC pipelines using tools like Debezium, Kafka Connect, or custom connectors. Familiarity with streaming systems such as Kafka, Kinesis, or Flink.
• Experience serving data using Trino or Presto in a multi-tenant environment.
• Proficiency in Python, Java or Scala, and Unix/Linux systems.
• Strong understanding of open table formats like Delta Lake or Apache Iceberg.
• Exposure to infrastructure as code (Terraform, CloudFormation) and CI/CD automation.

Preferred Mindset

• Platform-first thinker — designs systems for scale, abstraction, and reusability.
• Builder's bias — values automation, clean interfaces, and measurable SLAs.Deep collaborator — partners well across Data, Infra, and Engineering teams.
• Operational discipline — understands the impact of monitoring, logging,and reliability.

Why This Role Matters
We're looking for a Data Platform Engineer with deep systems engineering expertise and a passion for building reusable infrastructure. You'll help
architect and deliver the internal frameworks that power data ingestion, transformation, observability, and self-serve analytics across the company. Your work will be central to enabling scalable, multi-tenant ingestion pipelines (including CDC via Debezium), serving layers built on Trino, and robust tooling that lets teams move fast without compromising on data quality or platform
health.

What Makes This Opportunity Unique
Build platform tooling for multi-region, multi-tenant data infrastructure using modern open-source and cloud-native technologies.
Lead engineering efforts around CDC pipelines using Debezium, enabling reliable change capture from application databases.
Shape the developer experience for data engineers, from ingestion to publishing and consumption, with metadata-aware automation.
Drive the evolution of data serving infrastructure using Trino and open table formats like Delta Lake or Iceberg.
Work at the intersection of data engineering, infrastructure, and platform strategy, directly influencing core architectural decisions.

Responsibilities

• Design and implement foundational frameworks for ingestion, orchestration, schema validation, and metadata management.
• Build robust, scalable pipelines for Change Data Capture (CDC) using Debezium integrated with Kafka and Spark.
• Optimize data serving layers powered by Trino, including metadata syncing, security filtering, and performance tuning.
• Partner with SRE and Infra teams to build autoscaling, self-healing, and cost-optimized Spark jobs on AWS EMR.
• Implement observability features — logs, metrics, alerts — for critical platform services and data pipelines.
• Define and enforce standards for schema evolution, lineage tracking, and data governance.
• Automate platform operations using CI/CD pipelines, metadata-driven configurations, and infrastructure-as-code.

Desired Skills & Experience

• 5-7 years of experience in data platform, infrastructure, or backend systems engineering roles.
• Strong systems engineering fundamentals — distributed systems, fault tolerance, performance tuning.
• Expertise in Apache Spark with production experience on AWS EMR.
• Experience building or managing CDC pipelines using tools like Debezium, Kafka Connect, or custom connectors. Familiarity with streaming systems such as Kafka, Kinesis, or Flink.
• Experience serving data using Trino or Presto in a multi-tenant environment.
• Proficiency in Python, Java or Scala, and Unix/Linux systems.
• Strong understanding of open table formats like Delta Lake or Apache Iceberg.
• Exposure to infrastructure as code (Terraform, CloudFormation) and CI/CD automation.

Why This Role Matters 

This role powers Tekion’s AI‑native, end‑to‑end automotive platform by turning unified dealership data across DMS, CRM, Digital Retail, Service, and Payments into real‑time intelligence. You’ll operationalize a graph‑based contextual ecosystem so agents can retrieve the right context, enforce policy, and personalize experiences that drive measurable dealer outcomes. You’ll also build the resilient control layer - MCP and the LLM Gateway - that enables safe, cost‑efficient, multi‑provider LLM usage. Finally, you’ll define the standards for building, evaluating, deploying, and governing agentic systems so product teams can ship AI features quickly, safely, and at scale. In addition to enabling agentic systems powered by LLMs, this role also drives building the platform for classical ML models driving optimization across dealership operations.  

What Makes This Opportunity Unique 

This role offers direct, measurable impact on dealer outcomes and consumer experiences across Tekion’s Automotive Retail Cloud and Automotive Enterprise Cloud, with end‑to‑end ownership of an LLM control plane and gateway that serve multi‑tenant workloads under SLAs and, quality and cost guardrails. You’ll leverage a rich vertical dataset and domain graph spanning sales, service, parts, F&I, accounting, and consumer touchpoints to power context‑aware agents and retrieval‑augmented generation. You’ll also shape core levers - agent orchestration patterns, evaluation frameworks, and safety guardrails - so improvements in latency, reliability, evaluation quality, and safety translate into dealer KPIs like upsell, cycle time, CSAT, and service revenue.  You’ll also maintain and enhance the platform to support classical supervised and unsupervised ML models . 

Responsibilities 

• Build and run the LLM control plane/gateway: smart routing, rate limits/quotas, failover, and token/cost tracking. 

• Ship a unified API and SDKs (REST/gRPC) with normalized schemas, structured outputs, caching, and full observability (traces/logs/metrics). 

• Enforce safety and privacy by default: content filtering, prompt/response validation, and PII redaction. 

• Enable multi‑model, multi‑vendor use LLMs with automated canarying and versioning. 

• Own the agent runtime: tool registry, permissions, function calling, grounding, and retrieval. 

• Design orchestration patterns (sequential, planner‑executor, streaming) and manage agent state and long‑running workflows. 

• Enabling platform components for training and scoring pipelines for classical ML (e.g., XGBoost/LightGBM/linear/trees) and deep models; standardize experiment tracking and packaging. 

• Create components to Monitor model and data drift, retraining and tuning models as needed to maintain accuracy and relevance. 

• Add human‑in‑the‑loop review and safe‑actioning before agents touch dealer systems. 

• Evolve the domain graph and entity resolution; build reliable data ingestion pipelines. 

• Serve real‑time context to agents (profiles, inventory, pricing, appointments, service history) with access controls and lineage. 

• Power retrieval with hybrid search (graph + vector + keyword) and smart cache/TTL to balance accuracy, latency, and cost. 

• Run continuous offline/online evaluations for quality, factuality, bias, and safety for the platform sanity. 

• Define SLOs for latency (p50/p95), uptime, and cost view capabilities; enable autoscaling and spend controls. 

• Maintain a model/agent registry, versioning, approvals, audit trails, and reproducibility; support compliances where needed. 

Desired Skills & Experience 

• 4 - 5+ years building large‑scale data/ML or platform systems; strong software engineering fundamentals (Abstracted API design, concurrency, distributed systems). 

• Production experience with Python plus one of Java/Scala/Go; microservices and API design. 

• MLOps at scale: pipelines (Airflow/Kubeflow), tracking/registry (MLflow), CI/CD for models, A/B testing, shadow/canary, and online feature computation (Spark/Flink/Kafka). 

• Cloud and containers: AWS (preferred), plus Docker/Kubernetes; performance, reliability, and cost engineering in multi‑tenant SaaS. 

• Practical ML knowledge (feature engineering, training, evaluation, drift detection); experience deploying models that power user‑facing workflows. 

• Built or operated an LLM gateway/control plane: provider adapters, routing/policies, caching, quota/rate‑limit, cost and token accounting. 

• Agentic systems: tool use/function calling, orchestration frameworks, human‑in‑the‑loop, safety/guardrails, and online evaluation/telemetry. 

• Graph and retrieval: knowledge graphs (e.g., Neo4j/Neptune/TigerGraph), GraphQL, vector search (e.g., pgvector/Qdrant/Milvus), hybrid retrieval patterns. 

Preferred Mindset 

• Platform‑as‑product: obsess over developer experience, paved roads, and clear SLAs. 

• Thinks in systems - observability, fallback, access control are core, not afterthoughts. 

• Passionate about AI - enjoys enabling real-world LLM and agentic use cases. 

• Cost‑aware builder: you treat latency and dollars as first‑class metrics and design for graceful degradation. 

• Vendor‑agnostic thinker: choose the right model/provider per use case; build for portability and resilience. 

Why This Role Matters 

This role powers Tekion’s AI‑native, end‑to‑end automotive platform by turning unified dealership data across DMS, CRM, Digital Retail, Service, and Payments into real‑time intelligence. You’ll operationalize a graph‑based contextual ecosystem so agents can retrieve the right context, enforce policy, and personalize experiences that drive measurable dealer outcomes. You’ll also build the resilient control layer - MCP and the LLM Gateway - that enables safe, cost‑efficient, multi‑provider LLM usage. Finally, you’ll define the standards for building, evaluating, deploying, and governing agentic systems so product teams can ship AI features quickly, safely, and at scale. In addition to enabling agentic systems powered by LLMs, this role also drives building the platform for classical ML models driving optimization across dealership operations.  

What Makes This Opportunity Unique 

This role offers direct, measurable impact on dealer outcomes and consumer experiences across Tekion’s Automotive Retail Cloud and Automotive Enterprise Cloud, with end‑to‑end ownership of an LLM control plane and gateway that serve multi‑tenant workloads under SLAs and, quality and cost guardrails. You’ll leverage a rich vertical dataset and domain graph spanning sales, service, parts, F&I, accounting, and consumer touchpoints to power context‑aware agents and retrieval‑augmented generation. You’ll also shape core levers - agent orchestration patterns, evaluation frameworks, and safety guardrails - so improvements in latency, reliability, evaluation quality, and safety translate into dealer KPIs like upsell, cycle time, CSAT, and service revenue.  You’ll also maintain and enhance the platform to support classical supervised and unsupervised ML models . 

Responsibilities 

• Build and run the LLM control plane/gateway: smart routing, rate limits/quotas, failover, and token/cost tracking. 

• Ship a unified API and SDKs (REST/gRPC) with normalized schemas, structured outputs, caching, and full observability (traces/logs/metrics). 

• Enforce safety and privacy by default: content filtering, prompt/response validation, and PII redaction. 

• Enable multi‑model, multi‑vendor use LLMs with automated canarying and versioning. 

• Own the agent runtime: tool registry, permissions, function calling, grounding, and retrieval. 

• Design orchestration patterns (sequential, planner‑executor, streaming) and manage agent state and long‑running workflows. 

• Enabling platform components for training and scoring pipelines for classical ML (e.g., XGBoost/LightGBM/linear/trees) and deep models; standardize experiment tracking and packaging. 

• Create components to Monitor model and data drift, retraining and tuning models as needed to maintain accuracy and relevance. 

• Add human‑in‑the‑loop review and safe‑actioning before agents touch dealer systems. 

• Evolve the domain graph and entity resolution; build reliable data ingestion pipelines. 

• Serve real‑time context to agents (profiles, inventory, pricing, appointments, service history) with access controls and lineage. 

• Power retrieval with hybrid search (graph + vector + keyword) and smart cache/TTL to balance accuracy, latency, and cost. 

• Run continuous offline/online evaluations for quality, factuality, bias, and safety for the platform sanity. 

• Define SLOs for latency (p50/p95), uptime, and cost view capabilities; enable autoscaling and spend controls. 

• Maintain a model/agent registry, versioning, approvals, audit trails, and reproducibility; support compliances where needed. 

• Provide templates/CLIs, sandboxes, and docs so product teams can build and ship fast; mentor engineers and champion MLOps and AI safety best practices. 

Desired Skills & Experience 

• 5+ - 8+ years building large‑scale data/ML or platform systems; strong software engineering fundamentals (Abstracted API design, concurrency, distributed systems). 

• Production experience with Python plus one of Java/Scala/Go; microservices and API design. 

• MLOps at scale: pipelines (Airflow/Kubeflow), tracking/registry (MLflow), CI/CD for models, A/B testing, shadow/canary, and online feature computation (Spark/Flink/Kafka). 

• Cloud and containers: AWS (preferred), plus Docker/Kubernetes; performance, reliability, and cost engineering in multi‑tenant SaaS. 

• Practical ML knowledge (feature engineering, training, evaluation, drift detection); experience deploying models that power user‑facing workflows. 

• Built or operated an LLM gateway/control plane: provider adapters, routing/policies, caching, quota/rate‑limit, cost and token accounting. 

• Agentic systems: tool use/function calling, orchestration frameworks, human‑in‑the‑loop, safety/guardrails, and online evaluation/telemetry. 

• Graph and retrieval: knowledge graphs (e.g., Neo4j/Neptune/TigerGraph), GraphQL, vector search (e.g., pgvector/Qdrant/Milvus), hybrid retrieval patterns. 

Preferred Mindset 

• Platform‑as‑product: obsess over developer experience, paved roads, and clear SLAs. 

• Thinks in systems - observability, fallback, access control are core, not afterthoughts. 

• Passionate about AI - enjoys enabling real-world LLM and agentic use cases. 

• Cost‑aware builder: you treat latency and dollars as first‑class metrics and design for graceful degradation. 

• Vendor‑agnostic thinker: choose the right model/provider per use case; build for portability and resilience. 

Job Summary - Software Platform Architect

The Software Platform Architect is responsible for the high-level design, development, and evolution of scalable software platforms that support multiple products and business needs. This role collaborates with cross-functional teams—including engineering, product, and operations—to define technology standards, guide architectural decisions, and ensure the platform’s reliability, scalability, and security.

Key Responsibilities

• Lead the architecture and design of Integration and System Integrator Platform platforms, ensuring alignment with business objectives and technical strategy.
• Define and promote the use of architectural patterns, best practices, and standards across the engineering organization.
• Collaborate with development teams to translate business requirements into robust, scalable, and modular solutions.
• Evaluate and select appropriate technologies, frameworks, and tools to meet platform needs.
• Oversee integration with internal and third-party systems, ensuring data integrity and optimal performance.
• Conduct architecture reviews and provide technical guidance to engineering teams throughout the software development lifecycle.
• Identify and address potential bottlenecks, vulnerabilities, and system failures.
• Maintain documentation of architecture, processes, and best practices.
• Stay current with emerging technologies and industry trends, evaluating their potential impact on the platform.
• Mentor and coach engineering teams on architectural principles and practices.

Qualifications

• Bachelor’s or master's degree in computer science, Software Engineering, or related field.
• Experience in building at event driven applications at scale
• 10+ years of relevant experience in software development and architecture, with a focus on building platforms or large-scale systems.
• Experience is large scale messaging systems like Kafka
• Experience using Mongodb, ElasticSearch, MySQL, Postgresql
• Proven experience in building platform components using the java, SpringBoot.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and container orchestration (e.g., Kubernetes, Docker).
• Strong knowledge of APIs, microservices, data storage, and integration methodologies.
• Experience in SaaS platform architecture or development.
• Familiarity with DevOps practices and CI/CD pipelines.
• Experience with security, compliance, and regulatory requirements for enterprise software.
• Excellent problem-solving, communication, and leadership skills.
• Experience working in Agile environments.
• Experience in using AI tools (Augment, Cursor, Claude code)

Job Summary - Software Architect(Frontend)

The Software Architect – Frontend is responsible for leading the design, architecture, and implementation of modern, scalable, and maintainable frontend applications across our product suite. This role collaborates cross-functionally with designers, backend engineers, and product managers to ensure a seamless and consistent user experience, driving the adoption of best practices in frontend engineering and staying abreast of the latest technology trends.

Key Responsibilities:

· Define, design, and document the architecture for frontend applications, ensuring scalability, performance, accessibility, and maintainability.

· Establish and enforce the use of frontend development best practices, frameworks, and design patterns across teams.

· Collaborate closely with UX/UI designers to translate design concepts into functional, pixel-perfect user interfaces.

· Work with backend engineers to design and implement robust APIs and data integration strategies.

· Provide technical guidance and mentorship to frontend development teams, conducting code reviews and promoting a culture of continuous improvement.

· Optimize frontend performance for various browsers and devices, proactively identifying bottlenecks and areas for improvement.

· Evaluate and select technologies, frameworks, and tools to support the evolving needs of the frontend platform.

· Contribute to cross-team architectural initiatives and promote reusability across web and mobile solutions.

· Stay up to date with emerging trends in frontend development, evaluating their relevance and potential impact.

· Maintain thorough documentation of frontend architecture, workflows, and best practices.

Qualifications

· Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.

· 8+ years of professional frontend development experience, including significant experience in architectural or lead roles.

· Expertise in JavaScript, TypeScript, HTML5, CSS3, and modern frontend frameworks (e.g., React, Angular, Vue.js).

· Experience architecting and building large-scale, high-performance web applications.

· Strong understanding of web performance, security, accessibility standards (e.g., WCAG), and responsive design principles.

· Proven experience with state management solutions, component-based architecture, and modular design.

· Familiarity with backend integration, RESTful APIs, and micro frontends.

· Proficiency in build tools (e.g., Webpack, Babel), testing frameworks, and CI/CD pipelines.

· Excellent communication, leadership, and problem-solving abilities.

· Experience working in Agile or cross-functional teams.

· Experience with mobile web or hybrid app development.

· Knowledge of UI/UX best practices and collaboration with design systems teams.

· Contributions to open-source frontend projects or active involvement in frontend technology communities.

· Experience with internationalization, localization, and accessibility compliance in web applications.

· Experience in using AI tools (Augment, Cursor, Claude Code)

Job Description

Key Responsibilities

• Own end-to-end delivery of CI/CD pipelines, infrastructure as code, and deployment automation workflows. 
• Collaborate with application, infrastructure, and security teams to drive automation, system reliability, and operational excellence. 
• Establish and track key metrics (KPIs) for deployment frequency, change lead time, incident reduction, and operational resilience. 
• Ensure compliance and security requirements are met through robust tooling and effective processes (e.g., secrets management, vulnerability scanning, audit logging). 
• Manage resource planning, hiring, and performance reviews; foster a collaborative and inclusive team environment. 
• Report progress, risks, and key outcomes to senior leadership; contribute to technology, roadmaps and budget planning. 

Skills and Experience

• Bachelor’s/master’s in computer science, Engineering, or related field, or equivalent practical experience. 
• 5+ years of experience in DevOps or SRE roles, including large-scale automation and modern infrastructure management. 
• Deep experience with modern DevOps tooling and practices (e.g., Kubernetes, Terraform, Jenkins, GitHub Actions, AWS/GCP/Azure). 
• Proven expertise in automation, configuration management, monitoring/alerting, and incident response. 
• Solid understanding of security best practices for cloud-native environments (e.g., IAM, RBAC, encryption, compliance frameworks). 
• Strong communication skills, with the ability to effectively manage stakeholders, prioritize work, and build high-performing teams. 

Tekion is pioneering the digital transformation of the automotive industry, a sector that has remained largely undisrupted for over five decades. Our flagship platform, the Automotive Retail Cloud™, is the first and fastest cloud-native solution of its kind, fundamentally re-engineering the end-to-end automotive retail experience. By leveraging vast datasets, sophisticated machine learning, and advanced AI, we are creating a seamlessly integrated ecosystem connecting OEMs, retailers, and consumers. We are a globally distributed team of technologists and innovators dedicated to solving complex, high Impact challenges.

The Position

Tekion seeks an exceptionally talented and seasoned Senior Staff Applied Scientist to architect the future of our AI-driven solutions. The incumbent will assume a pivotal role in our organization, providing the strategic and technical stewardship necessary to translate cutting-edge research into tangible business value. The ideal candidate is a first-principles thinker, adept at navigating ambiguity, and possesses the intellectual curiosity to formulate and solve complex, unstructured problems. This is a seminal opportunity to influence the trajectory of our AI strategy and deliver transformative solutions at an enterprise scale. 

Key Responsibilities

• Strategic Direction: Architect and execute the strategic vision for applied AI and Machine Learning within Tekion, ensuring rigorous alignment with overarching business objectives and industry challenges.
• Innovation & Research Leadership: Steer the R&D roadmap by identifying and capitalizing on emergent technological opportunities. Drive innovation through the ideation and implementation of novel, scientifically-grounded solutions.
• Stakeholder Alignment: Serve as the principal technical liaison between the R&D organization and executive stakeholders, ensuring technical strategies are congruent with business imperatives.
• Cross-Functional Execution: Lead collaborations with product, engineering, and business units to define, develop, and integrate sophisticated machine learning systems into core products and services.
• Scientific Contribution: Contribute to the broader scientific community through publications in premier academic conferences and journals, enhancing Tekion's position as a center of innovation.
• Mentorship & Talent Development: Mentor and cultivate a culture of scientific rigor and innovation among applied scientists, guiding project execution and fostering professional growth.
• Impact Quantification: Establish robust frameworks for model evaluation and performance measurement. Quantify and articulate the business impact of machine learning initiatives to executive leadership.
• Governance & Best Practices: Institute and govern best practices for the entire machine learning lifecycle, including model development, validation, deployment, and monitoring, while championing ethical and responsible AI

Core Competencies & Technical Expertise
The successful candidate will demonstrate mastery in the following areas:

• Foundational Expertise: Deep, theoretical and practical expertise in Machine Learning, Deep Learning, Causal Inference, and Explainable AI.
• Statistical Rigor: Advanced proficiency in applied probability and statistics to derive and validate insights from complex, high-dimensional data.
• Deep Learning:
• Expert-level proficiency with frameworks such as TensorFlow, Keras, and PyTorch.
• Extensive experience implementing advanced neural network architectures.
• Practical application of Computer Vision (e.g., OpenCV) and Natural Language Processing (e.g., spaCy) methodologies.
• Large Language Models (LLMs): Demonstrated experience with Large Language Models, including advanced prompt engineering, fine-tuning, and deployment for specific business applications.
• Technical Proficiencies:
• Advanced programming skills in Python and mastery of SQL. Familiarity with distributed computing frameworks (e.g., Spark) is advantageous.
• Proficiency with cloud computing platforms (GCP, Azure, AWS).
• Expertise in experimental design (A/B testing, causal inference).
• Proficient in version control systems (Git).

Required Qualifications

• An advanced degree (Ph.D. or M.S. preferred) in a quantitative discipline such as Computer Science, Statistics, Operations Research, Physics, or a related field.
• A minimum of 11-14 years of post-academic experience in applied science or quantitative research roles, with a demonstrable history of translating complex models into tangible business value.
• A distinguished track record of solving difficult, business-critical problems using a scientific, data-driven approach.
• Proven experience in programming, data manipulation, and building models in a business context.
• Sophisticated data visualization and presentation skills, with the ability to distill complex analytical results into clear, actionable insights for diverse audiences.

Ideal Candidate Profile

• A profound intellectual curiosity and a systematic, first-principles approach to problem-solving.
• Domain expertise in the automotive industry or related fields such as finance or enterprise SaaS is highly desirable.
• Experience in specialized areas such as Recommendation Engines or advanced NLP is preferred.
• Exceptional communication and presentation capabilities, with a proven ability to distill complex technical concepts for executive-level audiences.
• A demonstrated ability to work collaboratively and effectively lead initiatives across multi-disciplinary teams.
• A commitment to continuous personal and team development within a collaborative, high-performance culture.

Perks and Benefits

• Competitive compensation
• Generous stock options
• Medical Insurance coverage
• Work with some of the brightest minds from Silicon Valley’s most dominant and successful companies

About Tekion

Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform. Our Automotive Retail Cloud (ARC), Automotive Enterprise Cloud (AEC), and Automotive Partner Cloud (APC) connect OEMs, retailers, and consumers through one seamless platform. Utilizing cutting-edge technology, big data, machine learning, and AI, Tekion is transforming the automotive retail ecosystem. We're inventing new technology along the way to overcome barriers and solve big problems, all while having a blast doing it with offices in North America, Asia, and Europe, Tekion employs around 3,000 people worldwide.

Tekion is pioneering the digital transformation of the automotive industry, a sector that has remained largely undisrupted for over five decades. Our flagship platform, the Automotive Retail Cloud™, is the first and fastest cloud-native solution of its kind, fundamentally re-engineering the end-to-end automotive retail experience. By leveraging vast datasets, sophisticated machine learning, and advanced AI, we are creating a seamlessly integrated ecosystem connecting OEMs, retailers, and consumers. We are a globally distributed team of technologists and innovators dedicated to solving complex, high Impact challenges.

The Position

Tekion seeks an exceptionally talented and seasoned Senior Staff Applied Scientist to architect the future of our AI-driven solutions. The incumbent will assume a pivotal role in our organization, providing the strategic and technical stewardship necessary to translate cutting-edge research into tangible business value. The ideal candidate is a first-principles thinker, adept at navigating ambiguity, and possesses the intellectual curiosity to formulate and solve complex, unstructured problems. This is a seminal opportunity to influence the trajectory of our AI strategy and deliver transformative solutions at an enterprise scale. 

Key Responsibilities

• Strategic Direction: Architect and execute the strategic vision for applied AI and Machine Learning within Tekion, ensuring rigorous alignment with overarching business objectives and industry challenges.
• Innovation & Research Leadership: Steer the R&D roadmap by identifying and capitalizing on emergent technological opportunities. Drive innovation through the ideation and implementation of novel, scientifically-grounded solutions.
• Stakeholder Alignment: Serve as the principal technical liaison between the R&D organization and executive stakeholders, ensuring technical strategies are congruent with business imperatives.
• Cross-Functional Execution: Lead collaborations with product, engineering, and business units to define, develop, and integrate sophisticated machine learning systems into core products and services.
• Scientific Contribution: Contribute to the broader scientific community through publications in premier academic conferences and journals, enhancing Tekion's position as a center of innovation.
• Mentorship & Talent Development: Mentor and cultivate a culture of scientific rigor and innovation among applied scientists, guiding project execution and fostering professional growth.
• Impact Quantification: Establish robust frameworks for model evaluation and performance measurement. Quantify and articulate the business impact of machine learning initiatives to executive leadership.
• Governance & Best Practices: Institute and govern best practices for the entire machine learning lifecycle, including model development, validation, deployment, and monitoring, while championing ethical and responsible AI

Core Competencies & Technical Expertise
The successful candidate will demonstrate mastery in the following areas:

• Foundational Expertise: Deep, theoretical and practical expertise in Machine Learning, Deep Learning, Causal Inference, and Explainable AI.
• Statistical Rigor: Advanced proficiency in applied probability and statistics to derive and validate insights from complex, high-dimensional data.
• Deep Learning:
  • Expert-level proficiency with frameworks such as TensorFlow, Keras, and PyTorch.
  • Extensive experience implementing advanced neural network architectures.
  • Practical application of Computer Vision (e.g., OpenCV) and Natural Language Processing (e.g., spaCy) methodologies.
• Large Language Models (LLMs): Demonstrated experience with Large Language Models, including advanced prompt engineering, fine-tuning, and deployment for specific business applications.
• Technical Proficiencies:
  • Advanced programming skills in Python and mastery of SQL. Familiarity with distributed computing frameworks (e.g., Spark) is advantageous.
  • Proficiency with cloud computing platforms (GCP, Azure, AWS).
  • Expertise in experimental design (A/B testing, causal inference).
  • Proficient in version control systems (Git).

Required Qualifications

• An advanced degree (Ph.D. or M.S. preferred) in a quantitative discipline such as Computer Science, Statistics, Operations Research, Physics, or a related field.
• A minimum of 11-14 years of post-academic experience in applied science or quantitative research roles, with a demonstrable history of translating complex models into tangible business value.
• A distinguished track record of solving difficult, business-critical problems using a scientific, data-driven approach.
• Proven experience in programming, data manipulation, and building models in a business context.
• Sophisticated data visualization and presentation skills, with the ability to distill complex analytical results into clear, actionable insights for diverse audiences.

Ideal Candidate Profile

• A profound intellectual curiosity and a systematic, first-principles approach to problem-solving.
• Domain expertise in the automotive industry or related fields such as finance or enterprise SaaS is highly desirable.
• Experience in specialized areas such as Recommendation Engines or advanced NLP is preferred.
• Exceptional communication and presentation capabilities, with a proven ability to distill complex technical concepts for executive-level audiences.
• A demonstrated ability to work collaboratively and effectively lead initiatives across multi-disciplinary teams.
• A commitment to continuous personal and team development within a collaborative, high-performance culture.

Job Description

Key Responsibilities

• Own end-to-end delivery of CI/CD pipelines, infrastructure as code, and deployment automation workflows. 
• Collaborate with application, infrastructure, and security teams to drive automation, system reliability, and operational excellence. 
• Establish and track key metrics (KPIs) for deployment frequency, change lead time, incident reduction, and operational resilience. 
• Ensure compliance and security requirements are met through robust tooling and effective processes (e.g., secrets management, vulnerability scanning, audit logging). 
• Manage resource planning, hiring, and performance reviews; foster a collaborative and inclusive team environment. 
• Report progress, risks, and key outcomes to senior leadership; contribute to technology, roadmaps and budget planning. 

Skills and Experience

• Bachelor’s/master’s in computer science, Engineering, or related field, or equivalent practical experience. 
• 5+ years of experience in DevOps or SRE roles, including large-scale automation and modern infrastructure management. 
• Deep experience with modern DevOps tooling and practices (e.g., Kubernetes, Terraform, Jenkins, GitHub Actions, AWS/GCP/Azure). 
• Proven expertise in automation, configuration management, monitoring/alerting, and incident response. 
• Solid understanding of security best practices for cloud-native environments (e.g., IAM, RBAC, encryption, compliance frameworks). 
• Strong communication skills, with the ability to effectively manage stakeholders, prioritize work, and build high-performing teams. 

Build and operate the production backbone that takes models from Applied Sciences (AS) and delivers reliable, low-latency ML services across Tekion’s DMS, CRM, Digital Retail, Service, Payments, and enterprise products. You’ll own pipelines, microservices, CI/CD, observability, and runtime reliability—working hand-in-hand with Applied Sciences and Product to turn ideas into measurable dealer and consumer impact. 

Why this Role Matters 

• Accelerate the rollout of LLM-powered and agent-driven features across Tekion products. 

• Enable agentic workflows that automate, reason, and interact on behalf of users and internal stakeholders. 

• Operationalize secure, compliant, and explainable LLM and agentic services at scale. 

• Convert Applied Sciences models into scalable, compliant, cost‑efficient production services. 

• Standardize how models are trained, validated, deployed, and monitored across Tekion products. 

• Power real-time, context-aware experiences by integrating batch/stream features, graph context, and online inference. 

What You’ll Do 

• Turn Applied Sciences prototype models (tabular, NLP/LLM, recommendation, forecasting) into fast, reliable services with well-defined API contracts. 

• Integrate with the LLM Gateway/MCP, prompt/config versioning. 

• Build and orchestrate CI/CD pipelines. 

• Review data science models; refactor and optimize code; containerize; deploy; version; and monitor for quality. 

• Collaborate with data scientists, data engineers, product managers, and architects to design enterprise systems. 

• Monitor, detect, and mitigate risks unique to LLMs and agentic systems. 

• Implement prompt management: versioning, A/B testing, guardrails, and dynamic orchestration based on feedback and metrics. 

• Design batch/stream pipelines (Airflow/Kubeflow, Spark/Flink, Kafka) and online features linked to our domain graph. 

• Build inference microservices (REST/gRPC) with schema versioning, structured outputs, and stringent p95 latency targets. 

• Manage the model/feature lifecycle: feature store strategy, model/agent registry, versioning, and lineage. 

• Instrument deep observability: traces/logs/metrics, data/feature drift, model performance, safety signals, and cost tracking. 

• Ensure real-time reliability: autoscaling, caching, circuit breakers, retries/fallbacks, and graceful degradation. 

• Develop templates/SDKs/CLIs, sandbox datasets, and documentation that make shipping ML the default path. 

Desired Skills and Experience 

• 8 - 10 years in ML engineering/MLOps or backend/platform engineering with production ML. 

• Experience with LLMs, retrieval systems, vector stores, and graph/knowledge stores. 

• Strong software engineering fundamentals: Python plus one of Java/Go/Scala; API design; concurrency; testing. 

• Hands-on with orchestration frameworks and libraries (LangChain, LlamaIndex, OpenAI Function Calling, AgentKit, etc.). 

• Knowledge of agent architectures (reactive, planning, retrieval-augmented agents), and safe execution patterns. 

• Pipelines and data: Airflow/Kubeflow or similar; Spark/Flink; Kafka/Kinesis; strong data quality practices. 

• Microservices and runtime: Docker/Kubernetes, service meshes, REST/gRPC; performance and reliability engineering. 

• Model ops: experiment tracking, registries (e.g., MLflow), feature stores, A/B and shadow testing, drift detection. 

• Observability: OpenTelemetry/Prometheus/Grafana; debugging latency, tail behavior, and memory/CPU hotspots. 

• Cloud: AWS preferred (IAM, ECS/EKS, S3, RDS/DynamoDB, Step Functions/Lambda), with cost optimization experience. 

• Security/compliance: secrets management, RBAC/ABAC, PII handling, auditability. 

Preferred Mindset 

• Product-oriented: You measure success by dealer and consumer outcomes, not just technical metrics. 

• Reliability- and safety-first: You move fast with guardrails, rollbacks, and clear SLOs. 

• Systems thinker: You design for multi-tenant scale, portability, and cost efficiency. 

• Collaborative: You translate between Applied Sciences, Product, and the Data & AI Platform; you document and teach. 

• Pragmatic: You automate the 80% and leave room for rapid experimentation.