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. 

This is a hybrid role, requiring 2 days per week in our Bangalore office

Reporting directly to the Director of Engineering, the Senior Software Engineer will guide the design and implementation of software solutions for ChannelOnline (COL). Our teams work across the front-end (Angular, jQuery, JSP), backend (Java), Data (Elastic, MySQL), DevOps, and QA. We need a seasoned engineer who can shape the long-term design of our system. This person will guide our teams in architecting product features in a way that moves us towards that vision. We have a team-oriented culture, value collaboration, self-direction, and continuous improvement. We learn from our experiences to always improve, and we have fun and celebrate our successes.

HOW WE’LL BE WINNING TOGETHER DAY TO DAY

• Establish and champion the high-level, long-term architectural strategy for COL
• Ensure software architecture aligns with the established technical strategy by establishing an Architecture Review Committee
• Advocate regulatory compliance, security policies, and processes in adherence to industry standards and Syndigo guidelines
• Drive continuous upgrades and enhancements of the various frameworks and tools used by the applications and the team
• Assess, identify, and mitigate common issues in the operations of the software suite preemptively
• Develop the engineering team's capability in architectural design and implementation through mentorship and enablement
• Lead the research, evaluation, and adoption of new technologies to advance and upgrade our solutions
• Partner with product and business leadership to ensure the technical strategy effectively addresses customer requirements and business goals
• Drive enhancements in engineering-wide practices, including CI/CD, testing, monitoring, security, and developer experience tooling

WE SHOULD TALK IF THIS SOUNDS LIKE YOU

• 4+ years of software development experience
• 1+ years of full-stack application development using object-oriented programming (Java) and modern frameworks
• 2+ years of experience designing and implementing solutions with Azure
• 1+ years of experience developing APIs for front-end applications
• 2+ years of experience with relational databases (MySQL) and large data sets
• 1+ years of experience leveraging and maintaining CI/CD tooling (GitHub Actions)
• 1+ years of experience with Perl or other scripting languages (preferred but not required)
• Experience leveraging automated database DDL (Liquibase, Flyway) (preferred but not required)
• Experience with object data stores (Azure Blob Storage) (preferred but not required)
• Experience building containerized applications (Docker) and deploying them to orchestration (Kubernetes) (preferred but not required)
• BS degree in Computer Science, a similar technical field, or equivalent practical experience

**This is a hybrid role, requiring 2 days per week in our Bangalore office**

Reporting directly to the Director of Engineering, the Lead Engineer will guide the design and implementation of the software solutions under the User Generated Content (UGC) business division. Our teams work across the front-end (React & Elm), backend (Java/Kotlin), Data (Snowflake, dbt, Tableau), DevOps, and QA. We need a seasoned engineer who can shape the long-term design of our system. This person will guide our teams in architecting product features in a way that moves us towards that vision. We have a team-oriented culture, value collaboration, self-direction, and continuous improvement. We learn from our experiences to always improve, and we have fun and celebrate our successes.

HOW WE’LL BE WINNING TOGETHER DAY TO DAY

• Establish and champion the high-level, long-term architectural strategy for the UGC division’s suite of products
• Ensure software architecture aligns with the established technical strategy by establishing an Architecture Review Committee
• Advocate regulatory compliance, security policies, and processes in adherence to Industry standards and 1World Sync guidelines
• Drive continuous upgrades and enhancements of the various frameworks and tools used by the applications and the team.
• Assess, identify, and mitigate common issues in the operations of the software suite preemptively
• Develop the engineering team's capability in architectural design and implementation through mentorship and enablement
• Lead the research, evaluation, and adoption of new technologies to advance and upgrade our solutions.
• Partner with product and business leadership to ensure the technical strategy effectively addresses customer requirements and business goals
• Drive enhancements in engineering-wide practices, including CI/CD, testing, monitoring, security, and developer experience tooling

WE SHOULD TALK IF THIS SOUNDS LIKE YOU 

• 10+ years of software development experience
• 7+ years of full-stack application development using Object oriented programming(Kotlin, Java) and modern frameworks
• 3+ years of experience designing and implementing solutions with AWS
• 3+ years of experience with event-driven development (SQS, SNS, Kafka, Kinesis, Pub/Sub) and idempotent patterns
• 5+ years of experience developing APIs for front-end applications
• 5+ years of experience with relational databases (PostgreSQL, MySQL) and large data sets
• 3+ years of experience leveraging and maintaining CI/CD tooling (CircleCI, GitHub Actions)
• 3+ years of experience leveraging infrastructure as code using Terraform
• Experience leveraging automated database DDL (Liquibase, Flyway) (preferred but not required)
• Experience with object data stores (AWS S3, Azure Blob Storage, GCP Cloud Storage) and/or document data stores (AWS DynamoDB, MongoDB, NoSQL) (preferred but not required)
• Experience building containerized applications (Docker) and deploying them to orchestration (AWS ECS, Kubernetes, Docker Swarm) (preferred but not required)
• Experience with Data engineering and Analytics (Snowflake, dbt, Tableau) (preferred but not required)
• AWS Solutions Architect Associate Certification (preferred but not required
• BS degree in Computer Science, a similar technical field, or equivalent practical experience

Role Overview

We are looking for domain-first, systems-oriented engineers who understand how real-world systems (in renewable energy, automotive, manufacturing, IoT, etc.) generate and use data — and can translate that into meaningful AI-driven workflows using DevRev.

As a Solutions Engineer, you will act as a trusted advisor, combining domain expertise, engineering depth, and problem-solving to help customers unlock value from their data through DevRev’s AI platform.

You will work closely with Sales, Product, and Engineering to design solutions that go beyond demos — enabling real-world automation, intelligence, and agentic workflows.

Key Responsibilities

• Partner with customers to deeply understand domain workflows, systems, and data flows (e.g., connected vehicles, factory systems, IoT environments)
• Translate real-world signals (sensor data, logs, events) into insights, workflows, and automated actions
• Build and showcase AI-driven workflows, automations, and agentic use cases
• Design and articulate end-to-end solutions (data ingestion → processing → decision → action)
• Act as a technical advisor, guiding customers on how DevRev can integrate into and enhance their existing systems
• Deliver tailored demos, PoCs, and solution walkthroughs grounded in real-world use cases (not generic SaaS demos)
• Support integrations with external systems via APIs, webhooks, and data pipelines
• Contribute to technical proposals, RFP/RFI responses, and solution architectures
• Stay current on industry trends (renewable energy, automotive, manufacturing, IoT, AI) and bring insights into customer conversations

Qualifications & Skills

Domain Expertise 

• Experience in renewable energy, automotive, manufacturing, industrial systems, or IoT environments
• Strong understanding of:
• Connected systems (vehicles, machines, robotics, embedded systems)
• Sensor ecosystems (CAN, telemetry, vision systems, IoT devices)
• Real-world data (signals, logs, time-series)
• Familiarity with manufacturing workflows (inspection, defect detection, production cycles).
• Ability to explain data flow from system → processing → business outcomes

Engineering Depth

• Strong system-level thinking
• Experience with:
• Data pipelines, APIs, integrations, distributed systems
• Experience in Python / TypeScript / JavaScript (or similar)
• Understanding of system design, reliability, and failure handling
• Exposure to AI/ML in industrial use cases (vision systems, predictive maintenance, log-stream analysis, etc.)

Problem Solving & Use Case Thinking

• Ability to translate data → insights → workflows → actions
• Experience designing end-to-end systems
• Strong analytical mindset to identify automation opportunities from real-world signals

AI & Tools

• Exposure to LLMs, RAG, AI agents
• Familiarity with prompt design and AI workflows
• Tools: Postman, GitHub, Jira, Cursor, Claude Code

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. 

At Tekion, we’re building the only cloud -native platform that is transforming the automotive retail industry, leap frogging it into the future and providing unparalleled customer experience. We’re creating seamlessly integrated, elegant, and intuitive solutions built with cutting edge technology and powered by Big Data, Machine Learning (ML)/ AI and Internet of Things (Connected vehicle to connected devices). We’re harnessing passion, entrepreneurial spirit, deep industry expertise and the latest technologies to create something very special. We’re inventing new technology along the way to overcome barriers and solve big problems, all while having a blast doing it!

Core Responsibilities:
• Taking ownership of customer issues reported and seeing problems through to resolution
• Researching, diagnosing, troubleshooting, and identifying solutions to resolve system issues
• Following standard procedures for proper escalation of unresolved issues to the appropriate internal teams
• Ability to understand the application both functionally and technically
• Ref er to internal database or external resources to provide accurate tech solutions
• Ensure all issues are properly logged
• Prioritize and manage several open issues at one time
• Prepare accurate and timely reports
• Document technical knowledge in the form of notes and manuals

Required Skills:
• Experience as an Application/Product/Customer Support Engineer.
• Ability to learn and master employer-specific software
• Complex problem solving
• Written and verbal communication skills
• Ability to diagnose & address application issues
• Basic understanding of IoT and software - preferred not mandatory
• Candidate should be ready for rotational shift perks and benefits

Hands on experience on chats / Ticketing tools

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

At Tekion, we’re building the only cloud -native platform that is transforming the automotive retail industry, leap frogging it into the future and providing unparalleled customer experience. We’re creating seamlessly integrated, elegant, and intuitive solutions built with cutting edge technology and powered by Big Data, Machine Learning (ML)/ AI and Internet of Things (Connected vehicle to connected devices). We’re harnessing passion, entrepreneurial spirit, deep industry expertise and the latest technologies to create something very special. We’re inventing new technology along the way to overcome barriers and solve big problems, all while having a blast doing it!

Core Responsibilities:
• Taking ownership of customer issues reported and seeing problems through to resolution
• Researching, diagnosing, troubleshooting, and identifying solutions to resolve system issues
• Following standard procedures for proper escalation of unresolved issues to the appropriate internal teams
• Ability to understand the application both functionally and technically
• Ref er to internal database or external resources to provide accurate tech solutions
• Ensure all issues are properly logged
• Prioritize and manage several open issues at one time
• Prepare accurate and timely reports
• Document technical knowledge in the form of notes and manuals

Required Skills:
• Experience as an Application/Product/Customer Support Engineer.
• Ability to learn and master employer-specific software
• Complex problem solving
• Written and verbal communication skills
• Ability to diagnose & address application issues
• Basic understanding of IoT and software - preferred not mandatory
• Candidate should be ready for rotational shift perks and benefits

Hands on experience on chats / Ticketing tools

Job Title : QA Engineer – IoT Devices
Work Model : (5 days)WFO
Experience Required : 1- 3 years  

About Netradyne:

Netradyne provides AI-powered technologies for fleet management and safer roads. An award-winning industry leader in fleet safety and video telematics solutions, Netradyne empowers thousands of commercial fleet customers across North America, Europe, and Asia to enhance their driver performance, reduce risk, and optimize operations.

Netradyne sets the standard among transportation technology companies for enhancing and sustaining road safety, with an industry-leading 25+ billion miles vision-analyzed for risk and an industry-first driver scoring system that reinforces safe behaviours. Founded in 2015, Netradyne is headquartered in San Diego with offices in San Francisco, Nashville, the UK and Bangalore.

For more details visit: www.netradyne.com

Job Overview:

We are looking for a QA Engineer to validate IoT devices across both hardware and software layers, with a strong focus on data‑intensive validation and real‑world operating conditions. The role requires hands‑on testing, automation, and analysis to ensure the reliability, performance, and quality of IoT systems at scale.

Roles and Responsibilities

As a QA Engineer, you will be responsible for:

• Validating IoT devices, including hardware modules and embedded software components
• Designing and implementing test scripts and automated validations to verify end‑to‑end device functionality
• Developing code snippets and simulations to validate device behaviour under varied real‑time and edge conditions
• Executing data‑driven testing by monitoring, analysing, and validating large data sets generated by IoT devices
• Building and enhancing systems or frameworks to proactively detect issues, anomalies, and regressions from data
• Collaborating closely with development and cross‑functional teams to identify defects early and improve overall product quality

Required Qualifications

• Strong programming skills in Python and Shell Scripting (mandatory)
• Hands‑on experience working with IoT devices or connected systems
• Experience in Big Data testing, data validation, or data quality analysis
• Exposure to testing methodologies including TDD and non‑functional testing (performance, reliability, scalability, etc.)
• Experience working with build systems and CI/CD pipelines

Desired Skills and Experience

• Hands‑on experience in developing or maintaining test frameworks
• Experience with embedded device testing
• Strong understanding of functional, integration, regression, usability, security, and performance testing
• Ability to derive test strategies, test approaches, and test plans for complex systems

Nice to Have

• Prior experience in IoT, automotive, or device‑centric domains
• Familiarity with Linux‑based environments and system‑level debugging

Equal Opportunity Employer Statement: Netradyne is an equal opportunity employer and are committed to creating an inclusive environment for all employees.

Additional Information: This position is based out in HM Tech Park, Whitefield Bangalore.​

Follow us on LinkedIn: For more information on our work culture, follow us on Netradyne: My Company | LinkedIn​

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. 

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. 

We are looking for a Senior NetSuite Developer to design, build, and enhance solutions within our ERP ecosystem. In this role, you will play a hands-on part in the development, customization, integration, and optimization of our NetSuite platform. You’ll partner closely with cross-functional teams across Finance, Operations, and Business Applications to create scalable and automated solutions that support our rapid growth.

Key Responsibilities

• Develop, test, and maintain custom scripts using SuiteScript 2.0 (and 1.0 where necessary) for automation, validation, and system extension.

• Build and optimize SuiteFlows, RESTlets, SuiteTalk APIs, and custom records to streamline operational and financial processes.

• Support configuration, customization, and administration of NetSuite modules, including ARM, SuiteBilling, Inventory, and Procurement.

• Create and maintain saved searches, dashboards, KPIs, and reports for business users and leadership visibility.

• Participate in integration efforts between NetSuite and third-party platforms such as Salesforce, Stripe, banks, and BI tools via middleware (e.g., Boomi, MuleSoft).

• Perform impact analysis and testing for system upgrades, new releases, and patch deployments.

• Collaborate with analysts, architects, and end users to gather requirements and translate them into scalable technical solutions.

• Write and maintain technical documentation, including design specs, test plans, and deployment runbooks.

• Apply best practices for development, testing, DevOps, and change management in NetSuite environments.

Qualifications

• 5+ years of NetSuite development experience with a strong technical foundation.

• Proven expertise in SuiteScript 2.0, SuiteFlow, SuiteTalk, saved searches, workflows, and RESTlets.

• Experience with NetSuite APIs (REST/SOAP) and middleware integration tools (Boomi, MuleSoft, Celigo, etc.).

• Deep understanding of key NetSuite processes such as Order-to-Cash, Procure-to-Pay, and Subscription Billing.

• Strong problem-solving skills and ability to independently drive complex technical projects.

• Excellent written and verbal communication skills; able to explain technical details to non-technical users.

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent work experience.

Preferred Qualifications

• NetSuite Certifications: SuiteFoundation, SuiteCloud Developer, or Administrator.

• Experience with NetSuite ARM (Advanced Revenue Management) and SuiteBilling.

• Knowledge of NetSuite OneWorld (multi-entity, multi-currency, global tax setup).

• Exposure to Salesforce or other CRM integrations with NetSuite.

• Familiarity with SaaS models: usage-based billing, subscription management, and revenue recognition.

• Understanding of SOX compliance and controls in a public company.

• Experience working in Agile/Scrum environments and collaborating in cross-functional squads.

• Ability to mentor junior developers and contribute to code reviews and architecture discussions.