Role

We’re looking for a Principal Software Development Engineer to join our team. Based out of our Bangalore office, you will report to the Senior Manager, Software Engineering within the Zero Trust Exchange department.

As a Principal Software Engineer you will build the backend systems that keep the product fast, reliable, and able to scale as usage grows. You will create microservices and event-driven workflows, and ensure data can be ingested and queried efficiently across relational, graph, and time-series databases. You will also drive cross-team execution by collaborating closely with multiple groups to deliver high-quality results on tight timelines.

What you’ll do (Role Expectations)

• Architect and deliver scalable microservices on EKS (GoLang): design, build, and operate production services that meet uptime, latency, and scale goals
• Build event-driven pipelines with Kafka and MQTT: implement asynchronous workflows that are resilient, observable, and easy to evolve as product needs change
• Design high-performance data models and access patterns: optimize ingest and querying across RDS MySQL, Neo4J, and ElasticSearch for correctness, speed, and cost efficiency
• Harden production readiness and operational excellence: establish monitoring, alerting, tracing, and on-call standards; drive root-cause fixes and reliability improvements
• Lead cross-team delivery and technical direction: align stakeholders, break down complex work, and execute quickly while maintaining a high bar for quality and security

Who You Are (Success Profile)

• You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. You adapt to what’s needed, navigating seamlessly between high-level strategy and hands-on execution.
• You are a problem-solver. You seek out challenges because you are energized by finding solutions, knowing that solving the hard problems delivers the biggest impact.
• You are a positive force. You approach hard problems with constructive energy and a 'can-do' spirit that is contagious, inspiring your team to stay focused on the solution.
• You are a pragmatic builder. You are obsessed with creating, iterating, and shipping. You aren't afraid to roll up your sleeves and build the first version yourself, balancing the drive for technical excellence with the need to deliver value to users quickly.
• You champion simplicity. You are skilled at distilling complex problems, user needs, and technical concepts into clear, simple, and actionable plans. You are a precise communicator who brings clarity and focus to every interaction.

What We’re Looking for (Minimum Qualifications)

• Senior-level backend engineering experience designing and shipping highly scalable microservices in production developed in Go
• Proven experience with event-driven, asynchronous architectures using messaging/streaming platforms such as Kafka
• Strong data engineering and persistence skills building efficient ingest and query patterns across relational databases (MySQL) plus at least one of ElasticSearch or Neo4J
• Hands-on experience running services on Kubernetes (ideally EKS), including deployment, troubleshooting, and operational ownership

What Will Make You Stand Out (Preferred Qualifications)

• Proven ability to profile and optimize distributed systems end-to-end (latency, throughput, resource usage), including performance tuning across services, databases, and messaging
• Strong track record designing debuggability/observability for distributed applications—structured logging, metrics, distributed tracing, and actionable dashboards/alerts for rapid incident triage
• Experience defining and operationalizing SLIs/SLOs (error budgets, alert policies, reliability reporting) and driving product decisions based on measured service health

#LI-Hybrid #LI-NT1

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. 

Job Title: Knowledge Graph

About Us

“Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery. 

WHY JOIN CAPCO?

You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.

MAKE AN IMPACT

Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.

#BEYOURSELFATWORK

Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.

CAREER ADVANCEMENT

With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.

DIVERSITY & INCLUSION

We believe that diversity of people and perspective gives us a competitive advantage.

Location - Bangalore

Experience - 6 to 10

Skills and Qualifications:

Preferred skills:

Role: Knowledge graphs

Mandatory skills: Knowledge-graph, RDF, Sparql, Neo4j

Staff Data Scientist – Supply Chain AI (Small Language Models)  

At Avathon, we build AI products that solve real industrial problems at scale. We believe the future of enterprise AI lies in small, focused, high-performance models—deeply grounded in domain context—not just large, generic systems. 

We’re looking for an experienced Data Scientist to join our Supply Chain AI team in India. In this role, you’ll design, fine-tune, and deploy optimized Small Language Models (SLMs) that power production-grade AI features across logistics, planning, inventory, and demand forecasting. 

What Makes This Role Unique 

This is a product-first data science role. You won’t just build models—you’ll own how they show up in real products used by large enterprises. Your work becomes the intelligence layer that sits on top of our graph and indexing systems and directly drives customer outcomes. 

What You’ll Do 

• Build and fine-tune SLMs/LLMs for production use cases such as semantic search, forecasting, contract intelligence, and conversational insights. Optimize models for performance, cost, and latency using PEFT, quantization, and efficient inference techniques 

• Embed supply chain knowledge into models by working closely with domain experts and product teams 

• Enable agentic workflows, allowing AI Assistants to execute planning, optimization, and decision-support tasks 

• Integrate models into products using custom Lambdas, and a Graph-based microservices architecture  

• Apply vector search and semantic reasoning to help customers navigate complex supply chain relationships 

• Work with real enterprise data—ERP, logistics, inventory, supplier, and contract datasets 

• Deploy and operate models using strong MLOps practices on platforms like AWS, GCP or AZURE 

• Measure and improve quality, reducing hallucinations and improving reliability through continuous evaluation 
• Partner with engineering, product, and operations teams to ship impactful AI features end-to-end 

What We’re Looking For 

• Experience working with SLMs (Mistral, Phi, Nemotron, Llama variants), applying parameter-efficient fine-tuning (LoRA/QLoRA) to optimize task-specific natural language query answering with high accuracy and minimized hallucinations. 

• Strong understanding of supply chain data and workflows (logistics, inventory, procurement, ERP systems) 

• Familiarity with Knowledge Graphs and graph-based data modeling, including designing and querying Neo4j graphs for relationship-driven insights. 

• Solid hands-on skills in Python, PyTorch/TensorFlow, GraphQL, and vector databases 

• 8+ years of experience in forecasting, optimization, supply chain analytics, or AI-driven products 

• Experience designing Retrieval-Augmented Generation pipelines using document chunking, metadata filtering, hybrid (dense + sparse) retrieval, and SLM/LLM grounding for enterprise knowledge search. 

• Hands-on experience building and tuning FAISS-based vector indexes (IVF, HNSW, PQ) for large-scale semantic similarity search with low-latency production deployment 

• Experience taking models from prototype to production 

• Bonus: reinforcement learning, MARL, graph neural networks, or probabilistic modeling 

• Clear communication skills and a strong sense of product ownership 

Why Avathon 

You’ll work on core AI products, not experiments—solving complex supply chain problems for global enterprises, while building from India as a key innovation hub. 

If you enjoy shipping AI that delivers measurable business value, this role is for you. 

About the Role

Workato is looking for a Principal Technical Architect, AI to join as a Field Specialist operating at the intersection of applied AI engineering and customer-facing technical strategy. This is a dual-mandate role with an approximately equal split:

Mandate 1 – AI Field Specialist (Customer-facing): You will work directly with enterprise customers and prospects in pre-sales and post-sales engagements to define and architect their AI strategies on Workato. This includes designing agentic automation solutions, advising on the latest approaches and best practices, scoping AI agent implementations, and helping customers move from proof-of-concept to production-grade autonomous systems.

Mandate 2 – Agentic CS Platform (Internal initiative): You will be a core architect and developer on Workato's autonomous Customer Success agent initiative, a production-grade system that captures complete customer context to support reasoning, and compounds intelligence over time. You will own the agent and memory architecture, evals and learning loop, and autonomy framework that power this system.

This is a role for someone who builds agentic systems to orchestrate LLMs into reliable, auditable, enterprise-grade agent architectures, and who can articulate that vision to a customer CIO and technical leaders as fluently as they can implement it in code.

Workato has a positive, diverse, and collaborative culture; we look for people who are curious, inventive, smart, hardworking, and who work to be a little better every day.

Responsibilities

AI Field Specialist

• Support as an expert AI architect in strategic enterprise engagements: roadmapping customer AI agent use cases and delivering technical deep-dives that demonstrate Workato's agentic capabilities.
• Lead architecture workshops with enterprise customers to define their AI automation strategy, including agent design, integration patterns, and governance frameworks.
• Advise customers on applied AI best practices: prompt engineering and agent orchestration patterns, evaluation and testing strategies for agent systems, confidence calibration, human-in-the-loop design, and continuous learning loops.
• Build reusable technical assets, such as reference architectures, solution blueprints, demonstration environments, and best-practice documentation, that enable the broader field team to position AI solutions.
• Develop and deliver technical collateral: architecture white papers, webinars, blog posts, conference talks, etc. that establish Workato's thought leadership in agentic automation.
• Partner with Product and Engineering to feed field insights back into the platform roadmap, ensuring customer-facing AI capabilities evolve based on real deployment patterns.
• Support global strategic accounts across the US, EMEA, and APAC geographies as needed for critical engagements.
• May require up to 20% global travel.

Agentic CS Platform

• Architect and implement core subsystems of the autonomous CS agent platform, including memory layers, the agent orchestration layer, and the decision trace architecture.
• Design and build a Customer Knowledge Graph as a structured, semantic network that models customers, users, policies, decisions, and operational artifacts as interconnected entities and relationships, enabling queryable representation of contextual states, and policy evaluations for governance, traceability, and decision optimization.
• Implement the confidence-based autonomy framework, including precedent matching, confidence scoring (precedent match, pattern recognition, data completeness, policy clarity), and policy-governed escalation routing.
• Define and design infrastructure architecture to ensure optimal performance and latency.
• Take scalable technology selection decisions.
• Establish evaluation frameworks: define metrics for decision quality, context accuracy, and learning velocity (autonomous threshold increases month over month).

Requirements

Qualifications & Experience

• B.Tech/BE or higher in Computer Science, Engineering, or related field. 
• 15+ years of total relevant experience in enterprise software architecture, design, and implementation.
• 8+ years of hands-on experience with Integration Platforms (MuleSoft, TIBCO, Oracle SOA, webMethods, or similar). Deep familiarity with iPaaS architecture patterns is essential.
• 2+ years of applied AI/Agents engineering experience, specifically in building systems that leverage LLMs and agent frameworks, orchestrating them into production applications.

Applied AI & Agent Architecture (Must-Have)

• Hands-on experience designing and building AI agent systems: multi-step reasoning pipelines, tool-use orchestration, and autonomous execution frameworks.
• Working knowledge of Python agent frameworks such as LangGraph, Claude Agent SDK, or equivalent. LangGraph experience with persisted state and human-in-the-loop patterns is strongly preferred.
• Experience with graph database design and implementation: entity modeling, relationship extraction, graph querying, and using graph structures for contextual retrieval. Neo4j/NetworkX equivalent experience is a plus.
• Practical experience with RAG architectures, vector databases, embedding strategies, and hybrid retrieval (vector + structured + graph).
• Understanding of evaluation and testing for AI systems: building evals, measuring agent quality, confidence calibration, A/B testing of prompts/pipelines, and regression testing for non-deterministic outputs.
• Familiarity with prompt engineering at production scale: structured prompting, chain-of-thought patterns, output parsing, retry/fallback strategies, and prompt versioning.
• Experience with observability for LLM systems: tracing, token/cost monitoring, and latency profiling. Familiarity with Langfuse, LangSmith, Phoenix, or similar tools.
• Experience with MCP (Model Context Protocol) standards for LLM-to-system integration.

Integration & Enterprise Architecture (Must-Have)

• Strong expertise in enterprise integration patterns: event-driven architectures, microservices orchestration, pub/sub messaging, and data synchronization.
• Deep working knowledge of APIs: RESTful, SOAP, webhooks, and data formats (JSON, XML).
• Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native services (Lambda/Functions, DynamoDB, Kafka/Kinesis, S3).
• Familiarity with enterprise applications: Salesforce, ServiceNow, SAP, Workday, NetSuite, or similar. Understanding how these systems participate in agentic workflows.
• Understanding of enterprise security and governance requirements: OAuth, SSO, data residency, SOX compliance, audit trails, and role-based access control.

Nice-to-Have

• Background in Customer Success platforms (Gainsight, ChurnZero) or CRM architecture.
• Prior experience in a customer-facing technical role (Solutions Architect, Field CTO, Technical Account Manager, or Pre-Sales Engineer) at a SaaS or platform company.
• Contributions to open-source AI/agent projects or published technical content in the applied AI space.

General Soft Skills

• Exceptional ability to communicate complex AI and architecture concepts to diverse audiences: from engineering teams to C-level executives: adapting depth and vocabulary to the room.
• Strong consultative instinct: able to listen to a customer's business problem, decompose it into architectural components, and propose a credible path forward in real-time.
• Entrepreneurial mindset with comfort operating in ambiguity: this role spans an internal build and an external advisory function, and requires someone who can context-switch and self-prioritize.
• Collaborative by default: able to work across Product, Engineering, Customer Success, Sales, and Marketing without needing formal authority.
• Excellent written communication: able to produce architecture documents, white papers, and technical briefs that are clear, precise, and persuasive.
• Fast learner who stays current with the rapidly evolving AI agent ecosystem and can distill emerging patterns into practical guidance.

(REQ ID: 2216)

Job Title: AI Full stack developer

 About Us

“Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the  British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery. 

WHY JOIN CAPCO? 

You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.

MAKE AN IMPACT

Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.

#BEYOURSELFATWORK

Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.

CAREER ADVANCEMENT

With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.

DIVERSITY & INCLUSION

We believe that diversity of people and perspective gives us a competitive advantage.

Job Description:

Job Overview:

We are seeking a seasoned AI Developer to lead the design and implementation of next-generation Agentic AI solutions. You won't just be building chatbots; you will be architecting autonomous systems where multiple specialized AI agents collaborate to solve complex, long-horizon business problems. You will leverage a multi-cloud strategy to deploy scalable, resilient, and "reasoning-heavy" AI workflows.

 Key Responsibilities

• Agentic Architecture: Design and develop autonomous agents capable of independent planning, tool-use (function calling), context & prompt engineering, and self-correction.
• Multi-Agent Orchestration: Implement LangGraph to manage task delegation, conflict resolution, and collaborative reasoning between specialized agents.
• Advanced RAG & Memory: Build sophisticated Retrieval-Augmented Generation (RAG) pipelines with episodic and semantic memory to ensure agents maintain context over long interactions.
• Cloud-Native Deployment: Architect and deploy AI services across AWS (Bedrock/SageMaker), Azure (OpenAI Service/Foundry), ensuring high availability and cost optimization.
• Guardrails & Ethics: Implement rigorous evaluation frameworks (e.g., Ragas, TruLens) and safety guardrails to prevent hallucinations and ensure responsible agent behavior.
• Performance Tuning: Optimize LLM latency and throughput using techniques like prompt caching, quantization, and specialized model routing.
• Prompt engineering & Context engineering.

Key Skills:

• Core AI: 4+ years of professional experience in AI/ML, with at least 1.5+ years focused specifically on Generative AI and LLM orchestration.
• Agent Frameworks: Hands-on expertise with LangGraph. Proven ability to build "loops" and "state machines" rather than just linear chains.
• Programming: Mastery of Python (Asyncio, Pydantic, FastAPI). Experience with TypeScript/Node.js is a significant plus.
• Cloud Infrastructure:
• AWS: Bedrock, Lambda, Step Functions, S3.
• Azure: Azure OpenAI, AI Search, CosmosDB, PostgreSQL.
• Data & Memory: Proficiency with Vector Databases like Pinecone, Weaviate, Milvus, or pgvector. Understanding of graph databases (Neo4j) for knowledge-graph-enhanced RAG.
• DevOps/AIOps: Experience with Docker, Kubernetes, and CI/CD pipelines specifically for AI (LLMOps).

 Preferred Qualifications

• Contributions to open-source AI frameworks or research publications in MAS.
• Experience with Model Context Protocol (MCP) or Agent-to-Agent (A2A) communication standards.
• Familiarity with fine-tuning techniques (LoRA, QLoRA) for specialized agent tasks.

Job Overview:

We are seeking a seasoned AI Developer to lead the design and implementation of next-generation Agentic AI solutions. You won't just be building chatbots; you will be architecting autonomous systems where multiple specialized AI agents collaborate to solve complex, long-horizon business problems. You will leverage a multi-cloud strategy to deploy scalable, resilient, and "reasoning-heavy" AI workflows.

 Key Responsibilities

• Agentic Architecture: Design and develop autonomous agents capable of independent planning, tool-use (function calling), context & prompt engineering, and self-correction.
• Multi-Agent Orchestration: Implement LangGraph to manage task delegation, conflict resolution, and collaborative reasoning between specialized agents.
• Advanced RAG & Memory: Build sophisticated Retrieval-Augmented Generation (RAG) pipelines with episodic and semantic memory to ensure agents maintain context over long interactions.
• Cloud-Native Deployment: Architect and deploy AI services across AWS (Bedrock/SageMaker), Azure (OpenAI Service/Foundry), ensuring high availability and cost optimization.
• Guardrails & Ethics: Implement rigorous evaluation frameworks (e.g., Ragas, TruLens) and safety guardrails to prevent hallucinations and ensure responsible agent behavior.
• Performance Tuning: Optimize LLM latency and throughput using techniques like prompt caching, quantization, and specialized model routing.
• Prompt engineering & Context engineering.

Key Skills:

• Core AI: 4+ years of professional experience in AI/ML, with at least 1.5+ years focused specifically on Generative AI and LLM orchestration.
• Agent Frameworks: Hands-on expertise with LangGraph. Proven ability to build "loops" and "state machines" rather than just linear chains.
• Programming: Mastery of Python (Asyncio, Pydantic, FastAPI). Experience with TypeScript/Node.js is a significant plus.
• Cloud Infrastructure:
• AWS: Bedrock, Lambda, Step Functions, S3.
• Azure: Azure OpenAI, AI Search, CosmosDB, PostgreSQL.
• Data & Memory: Proficiency with Vector Databases like Pinecone, Weaviate, Milvus, or pgvector. Understanding of graph databases (Neo4j) for knowledge-graph-enhanced RAG.
• DevOps/AIOps: Experience with Docker, Kubernetes, and CI/CD pipelines specifically for AI (LLMOps).

 Preferred Qualifications

• Contributions to open-source AI frameworks or research publications in MAS.
• Experience with Model Context Protocol (MCP) or Agent-to-Agent (A2A) communication standards.
• Familiarity with fine-tuning techniques (LoRA, QLoRA) for specialized agent tasks.

The Role

A Cloud Operations Engineer is comfortable troubleshooting server & database technical issues and can reliably manage and monitor the customer’s Neo4j database environment, taking a holistic view of the health of the environment, to maximize the application and infrastructure uptime.

This role is instrumental in ensuring our customers are successfully deploying and managing Neo4j in their production environments. It is well suited to an individual with strong technical skills who enjoys solving problems as much as collaborating directly with customers.

Responsibilities

• Participate in the On-Call rotation, as a member of the Cloud Managed Services team, and respond to incidents that impact Neo4j cloud infrastructure availability to provide the necessary support for customer cases.
• Work as a member of the Cloud Managed Services Team during business hours, with occasional off-hours scheduled work, to support and manage the Neo4j database infrastructure of the customers and handle and resolve escalations related to cloud hosted environments from customers and/or internal teams. 
• Improve, document, and automate operational processes (such as deployments and upgrades) to make them as efficient as possible.
• Gather and analyze metrics from operating systems as well as Neo4j databases to assist in performance tuning and incident diagnostics.
• Document the incidents, procedures, and outcomes of customer communications. Assist in building the Cloud Managed Services knowledge base.
• Install and upgrade Neo4j infrastructure (servers, storage, databases) for our Cloud Managed Service customers.
• Troubleshoot cluster issues across multiple servers, data centers and regions, in a variety of clouds (AWS, Azure, Google Cloud), virtual, and bare metal environments.
• Collaborate and communicate effectively within Customers, Customer Success, Product Engineering, and other Neo4j teams to understand and resolve customer issues completely and expediently.
• Proactive approach to identifying problems, performance bottlenecks, and areas for improvement is necessary to succeed in this position.

Job Requirements and Qualifications

• 5+ years of cloud infrastructure administrator experience is required.
• Bachelor's or equivalent work experience degree in Computer Science or Computing Systems equivalent field. Background in math will be a plus.
• Experience working with Linux operating systems such as RedHat / CentOS / Amazon Linux is required.
• Significant prior experience with at least one of the Big 3 Cloud providers (AWS/GCP/Azure) is required.
• Must have command-level fluency in Linux environments.
• Familiarity with Linux / Windows diagnostics and tuning will be a plus.
• Experience with Ansible, Terraform, and Kubernetes is desirable.
• Experience working with Nagios and Prometheus is desirable.
• Neo4j Certified Professional certification is desirable.
• Must be proficient in the use of Microsoft Office Applications (Outlook / Word / Excel).
• Must be detail oriented, collaborate well with other team members, have excellent verbal and communication skills, and thrive in a fast paced, high-energy, multi-tasking environment.

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. 

The Developer Advocate will be the bridge between our developer community and the company. This role is responsible for promoting our products and technologies to developers, and advocating for developer needs within the organization. 

The ideal candidate balances leadership and participation, lifting a community up with equal parts inspiration and celebration. You are an active learner and educator, presenter and listener, builder and champion.

We are looking for a candidate who is geographically located in India and can regularly attend and speak at meetups.

Key Responsibilities:

Developer Engagement:

• Build and nurture relationships with developers, both internally and externally.
• Act as a trusted resource and advocate for developers, addressing their questions and concerns.
• Collaborate with the developer community through forums, social media, and other channels.

Product Knowledge and Expertise:

• Develop a deep understanding of our products and technologies.
• Stay up-to-date with industry trends, emerging technologies, and competitors.
• Use product knowledge to provide valuable insights and support to developers.

Developer Support:

• Provide technical support to developers, helping them overcome challenges and issues.
• Create and maintain documentation, tutorials, and resources to aid developers.
• Offer hands-on assistance and guidance to developers during product integration.

Advocacy and Feedback:

• Serve as the voice of developers within the organization, advocating for their needs and preferences.
• Provide actionable feedback and insights from the developer community to influence product development.
• Collaborate with cross-functional teams to enhance the developer experience.

Community Building:

• Organize and participate in developer events, webinars, and meetups.
• Foster a sense of community among developers, encouraging collaboration and knowledge sharing.
• Identify and engage developer influencers and champions.

Content Creation:

• Create developer-focused content, including blog posts, technical articles, videos, and presentations.
• Ensure that content aligns with the interests and needs of the developer community.
• Promote and share valuable content through various channels.

Qualifications:

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 3+ years of experience in developer advocacy, developer relations, or a related technical role.
• Fluent in language for target communities (English)
• Strong technical knowledge and familiarity with developer tools, APIs, and technologies.
• Public github repos, blogs and/or videos
• Exceptional communication and presentation skills.
• Empathy for developers and their needs.
• Passion for technology and developer communities.
• Strong networking and relationship-building skills.

About this Role

• In this role, you will contribute to Neo4j's accelerated growth and will ensure strong collaboration between Sales, Marketing, Business Development, Revenue Intelligence and other departments across Neo4j, to help design & implement revenue growth strategies
• The role will require strong cross-functional partnership to provide scalable processes, strategies and tactics to enable us to grow and capture market share across the world
• You will be an essential member of the wider Revenue Operations team, and a subject matter expert in Sales Operations
• Develop key metrics and implement strategic initiatives to ensure strong alignment with company goals 
• Develop automated efficiencies wherever possible and create repeatable processes scale the effectiveness of our sellers and Revenue Strategy teams
• Help with determining and prioritizing operational priorities connected to customer acquisition, growth, and ongoing success as we work towards our revenue and efficiency goals
• Establish high levels of quality, accuracy, and process consistency in planning, forecasting, and other efforts run by Sales Operations
• Understand and assist with annual planning, territory design, sales compensation planning, sales quota allocation, compensation policies, and general sales alignment
• Execute effective change management and communications for technology implementations and automation within the Sales organization
• Partner with the Office of the CRO & Revenue Intelligence teams to deliver core quantitative analysis; develop reporting and dashboards to show performance, trends, opportunities, and risks across the customer life cycle

Qualifications

• 5+ years of experience in Sales Operations or Revenue Operations with a strong track record of improving revenue generating processes through expertise, consistency, and cross functional execution
• Highly organized and continuously improvement-driven, focused on making lasting impact across people, processes, and systems
• Trusted partner to Sales and overlay teams, known for translating business goals into operational strategies that help GTM teams scale
• Deep cross-functional experience collaborating across Sales, Marketing, Finance, and Systems teams to drive strategy, execution, and alignment
• Excellent communicator with a collaborative, solution-oriented mindset that fosters trust and momentum across teams
• Skilled at providing sales leaders with actionable insights, performance trends, and pipeline visibility to support strategic decision making
• Operates independently in fast paced, ambiguous environments with a bias for informed action and execution
• Proven success in designing and implementing scalable processes that increase seller productivity and unlock growth opportunities
• Expert in GTM workflows related to core sales technology systems to support internal workflows across AI, Salesforce and key third-party tools (Gong, Zoominfo, Outreach,etc.) with a focus on continuous optimization and driving adoption to improve sales workflows
• Strong presentation and storytelling abilities able to distill insights and influence stakeholders at all levels, from executive leadership to frontline teams

Role Summary

Help AI-focused startups in Neo4j’s Startup Program succeed with graph + LLM solutions so they adopt Neo4j deeply and grow into long-term, high-value customers.

This is a highly technical, customer-facing role focused on helping AI startups onboard successfully, design effective solutions, and scale their Neo4j use over time. The role blends solution architecture, technical advisory, and ongoing customer engagement to support startups from early experimentation through production adoption.

Key Responsibilities

Technical Leadership for Startup Customers

• Serve as the primary technical point of contact for a portfolio of AI startups in the program.
• Guide teams in designing, building, and deploying Neo4j-based architectures.
• Advise on best practices for Neo4j usage across Aura and self-managed environments, including scaling, performance, and production readiness.
• Troubleshoot implementation challenges and help customers make sound technical tradeoffs.

Graph + AI / LLM Solution Design

• Help startups design and implement LLM-enabled solutions using Neo4j, including RAG, knowledge graphs, and agent-based architectures.
• Translate customer AI requirements into concrete Neo4j-centered architectures, patterns, and implementation guidance.
• Work with common AI frameworks and tooling such as LangChain, LlamaIndex, PydanticAI or ADK, agent frameworks, vector search, and open-source and commercial LLMs.
• Identify and promote repeatable graph + AI solution patterns across the startup portfolio.

Customer Success and Technical Relationship Ownership

• Own the ongoing technical relationship with assigned startups.
• Run regular check-ins, track progress, and proactively identify risks or blockers.
• Help customers move from early experimentation to successful production adoption.

Cross-Functional Collaboration

• Partner closely with the Field AI team to align on architectures, patterns, and technical strategy.
• Feed real-world startup requirements, usage patterns, and product gaps back to Product and Field teams.
• Bridge product gaps where needed through third-party tools, integrations, and pragmatic solution design.

Enablement and Knowledge Sharing

• Contribute to internal enablement content, examples, office hours, and best practices for graph + AI patterns.
• Share lessons learned from startup engagements with broader Field teams.
• Help standardize playbooks, templates, and reference implementations for the Startup Program.

Required Experience and Skills

Neo4j and Graph Technology

• Strong hands-on experience with Neo4j or other graph database, including data modeling, Cypher, clustering/topology, and performance tuning.
• Demonstrated ability to debug and optimize real-world implementations, including queries, data ingestion, and infrastructure decisions.

Solution Engineering and Architecture

• Background in solutions engineering, solution architecture, professional services, customer success, or a similar customer-facing technical role.
• Ability to move fluidly between high-level architecture and detailed implementation guidance.
• Experience working with multiple stakeholders to align on a practical technical approach.
• Awareness of sales processes and expansion motions is a plus.

AI / LLM Expertise

• Experience or strong interest in AI/LLMs and agentic architectures.
• Practical exposure to modern AI tooling, including agent frameworks, vector databases, and open-source LLMs.
• Familiarity with some of the following: LangChain, LlamaIndex, RAG patterns, PydanticAI, ADK, or similar frameworks. Docling, Unstructured.io , PyMuPDF.
• Strong curiosity and aptitude to stay current in the graph + AI landscape.

Customer-Facing Communication

• Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.
• Comfortable leading customer calls, asking probing questions, and constructively challenging design decisions.
• Proven customer-facing experience in a technical role.

General Technical Background

• Strong technical foundation, ideally from pre-sales engineering, solutions engineering, or professional services.
• Proficiency in programming languages such as Python or Java.
• Familiarity with cloud platforms such as AWS, Azure, or GCP.
• Experience with containerization and modern application architectures.
• Ability to work independently and collaboratively in a fast-paced environment.

Qualifications

• Bachelor’s degree in Computer Science, Information Technology, or a related field.
• Proven experience as a Solutions Engineer, Sales Engineer, Solutions Architect, or in a similar technical customer-facing role.

Neo4j Technical Onboarding Engineers play a critical role in supporting our customers, early in their Neo4j journey, to ensure the success of their project. 

As a Technical Onboarding Engineer, you will lead new customers from signed contracts to getting them started on planning all aspects to build a production-ready Neo4j environment. You’ll provide guidance on critical aspects of their project (cloud provisioning, security, networking, data import, app integration) along with enablement (workshops, docs, handover). You will shape onboarding plans aligned to our onboarding tiers, orchestrate delivery across workstreams, and ensure a predictable time‑to‑value - partnering closely with Professional Services and Cloud Operations teams.

For AuraDB customers, you will leverage the service’s automation—provisioning, upgrades, and backups—while focusing on data modeling, connectivity, and application success. For self‑managed customers, you will apply infrastructure‑as‑code and platform engineering practices to deliver secure, resilient clusters.

Responsibilities Overview

• Plan & Kickoff — drive technical discovery; confirm success criteria/KPIs; align on the onboarding plan, milestones, and risks.
• Facilitate with templates, planning & guidance
• Environment Provisioning - Neo4j AuraDB tenants or Neo4j clusters in AWS/Azure/GCP.
• Environment Security —security groups, TLS/SSL, encryption at rest/in transit, and baseline hardening.
• Identity & Access —SSO (OIDC/SAML); design RBAC groups; enforce least‑privileged access.
• Networking —IP allows lists; set up VPC/VNet peering or PrivateLink/Private Endpoint
• Data Onboarding —bulk imports; simple transforms and validation; CDC/streaming for higher tiers.
• Application Integration — drivers; tune connection pooling, retries, and timeouts. 
• Query Optimization - Cypher/query best practices.
• Observability & Operations — wire metrics/logs; alerts; backup/restore planning
• Enablement & Handover —architecture diagrams, ADRs and a next‑step backlog.
• Continuous Improvement — capture lessons learned & evolve the onboarding playbook.

Job Requirements & Qualifications

• Customer‑facing delivery: clear communication, scoped planning, risk management, crisp documentation.
• Cloud: depth in at least one major cloud (AWS, Azure, or GCP) and familiarity with the others.
• IaC: Terraform as primary; Ansible or similar configuration tooling.
• Linux & containers: strong CLI; Kubernetes or managed container platforms a plus.
• Networking: VPC/VNet, routing, SG/NSG, peering, PrivateLink/private endpoints, DNS.
• Identity & security: SSO (OIDC/SAML), RBAC design, secrets management (e.g., AWS Secrets Manager, Azure Key Vault).
• Data movement: bulk imports and at least one ETL/CDC tool or framework.
• App integration: experience with Neo4j drivers/SDKs, connection tuning, and performance troubleshooting.
• Neo4j or other graph database experience; Cypher query tuning.
• Observability: Grafana/Prometheus, CloudWatch/Cloud Logging, Datadog, or Splunk.
• CI/CD: GitHub Actions, GitLab CI, or Jenkins; release practices and environment promotion.
• Compliance/security exposure: SOC 2, GDPR, HIPAA; data residency patterns.

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. 

About the opportunity

We are looking for a passionate Senior Software Engineer to join Veza’s Graph team. You will play a key role in designing, improving, and maintaining the robust and scalable code behind the Veza Access Graph, collaborating closely with cross-functional teams to shape the future of our products and deliver exceptional identity and access management experiences to our customers. This is an exciting opportunity to contribute to the growth and success of a high-impact startup while working on cutting-edge technologies! 

You Will:  

• Develop, maintain and scale Graph features to directly and indirectly support actionable customer intelligence across various products.
• Drive the performance, scalability, and reliability of the backend services and APIs for our platform.
• Collaborate with product managers, other software engineers, and security experts to build product features that meet business objectives and security standards.
• Continuously optimize and improve your work through code and design reviews, testing, and refactoring.
• Implement logging, monitoring, and auditing capabilities to ensure visibility and compliance with regulatory requirements and operational health metrics.
• Stay current with industry trends and emerging technologies across different fields and leverage them to drive innovation and improve our platform.

You Have:

• Education:
• A bachelor's or master's degree in computer science, engineering, or a related field.
• Experience:
• Proven experience (minimum 5+ years) in professional backend software development with a focus on data modeling, data management, identity, security, or related areas.
• A history with the development of databases, storage layers, query engines or adjacent tooling.
• Experience with complex relational data models in OLAP systems and/or graph database systems such as Neo4j.
• Deep knowledge of relational algebra and graph algorithms.
• A familiarity with caching mechanisms and distributed systems.
• A proven ability to develop and test code in one or more modern backend programming languages such as Golang or Kotlin.
• Experience designing and implementing RESTful APIs in microservice architectures.
• Experience with cloud platforms, such as AWS, Azure, or Google Cloud Platform, and containerization and orchestration technologies, such as Docker and Kubernetes, is a plus.

You Are:

• An excellent problem-solver with high attention to detail.
• Someone who can work both independently and collaboratively in a fast-paced environment.
• A strong communicator with the ability to relate technical concepts to both technical and non-technical stakeholders.
• A mentor who helps others become the best versions of themselves.
• A pleasure to work with!

Staff Data Scientist – Supply Chain AI (Small Language Models)  

At Avathon, we build AI products that solve real industrial problems at scale. We believe the future of enterprise AI lies in small, focused, high-performance models—deeply grounded in domain context—not just large, generic systems. 

We’re looking for an experienced Data Scientist to join our Supply Chain AI team in India. In this role, you’ll design, fine-tune, and deploy optimized Small Language Models (SLMs) that power production-grade AI features across logistics, planning, inventory, and demand forecasting. 

What Makes This Role Unique 

This is a product-first data science role. You won’t just build models—you’ll own how they show up in real products used by large enterprises. Your work becomes the intelligence layer that sits on top of our graph and indexing systems and directly drives customer outcomes. 

What You’ll Do 

• Build and fine-tune SLMs/LLMs for production use cases such as semantic search, forecasting, contract intelligence, and conversational insights. Optimize models for performance, cost, and latency using PEFT, quantization, and efficient inference techniques 

• Embed supply chain knowledge into models by working closely with domain experts and product teams 

• Enable agentic workflows, allowing AI Assistants to execute planning, optimization, and decision-support tasks 

• Integrate models into products using custom Lambdas, and a Graph-based microservices architecture  

• Apply vector search and semantic reasoning to help customers navigate complex supply chain relationships 

• Work with real enterprise data—ERP, logistics, inventory, supplier, and contract datasets 

• Deploy and operate models using strong MLOps practices on platforms like AWS, GCP or AZURE 

• Measure and improve quality, reducing hallucinations and improving reliability through continuous evaluation 
• Partner with engineering, product, and operations teams to ship impactful AI features end-to-end 

What We’re Looking For 

• Experience working with SLMs (Mistral, Phi, Nemotron, Llama variants), applying parameter-efficient fine-tuning (LoRA/QLoRA) to optimize task-specific natural language query answering with high accuracy and minimized hallucinations. 

• Strong understanding of supply chain data and workflows (logistics, inventory, procurement, ERP systems) 

• Familiarity with Knowledge Graphs and graph-based data modeling, including designing and querying Neo4j graphs for relationship-driven insights. 

• Solid hands-on skills in Python, PyTorch/TensorFlow, GraphQL, and vector databases 

• 8+ years of experience in forecasting, optimization, supply chain analytics, or AI-driven products 

• Experience designing Retrieval-Augmented Generation pipelines using document chunking, metadata filtering, hybrid (dense + sparse) retrieval, and SLM/LLM grounding for enterprise knowledge search. 

• Hands-on experience building and tuning FAISS-based vector indexes (IVF, HNSW, PQ) for large-scale semantic similarity search with low-latency production deployment 

• Experience taking models from prototype to production 

• Bonus: reinforcement learning, MARL, graph neural networks, or probabilistic modeling 

• Clear communication skills and a strong sense of product ownership 

Why Avathon 

You’ll work on core AI products, not experiments—solving complex supply chain problems for global enterprises, while building from India as a key innovation hub. 

If you enjoy shipping AI that delivers measurable business value, this role is for you.