Hiring.fm
Tekion

Staff Machine Learning Engineer

Bangalore, India Full Time

About Tekion:

Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3,000 people across North America, Asia and Europe.

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.