Staff Machine Learning Engineer
Core
Build and operate the production backbone for LLM-powered and agent-driven features, converting research models into reliable, low-latency ML services across automotive retail products.
Role type
Staff Machine Learning Engineer (MLOps/Platform)
Builds
Scalable, compliant, and cost-efficient production ML services, pipelines, and microservices for DMS, CRM, and Digital Retail products.
Domain
Automotive retail technology, Large Language Models (LLM), Agentic AI systems
Deliverable
production ML models | product features | infrastructure
Required skills
Python, Java/Go/Scala, LLMs, retrieval systems, vector stores, graph/knowledge stores, orchestration frameworks (LangChain, LlamaIndex), agent architectures, CI/CD pipelines, microservices, Docker/Kubernetes, REST/gRPC, model ops, observability, cloud infrastructure (AWS)
Preferred skills
Prompt management, A/B testing, guardrails, dynamic orchestration, feature store strategy, model registry, lineage tracking, real-time reliability engineering, cost optimization
Technologies
LangChain, LlamaIndex, OpenAI Function Calling, AgentKit, Airflow, Kubeflow, Spark, Flink, Kafka, Kinesis, MLflow, OpenTelemetry, Prometheus, Grafana, AWS (ECS/EKS, S3, RDS, DynamoDB, Step Functions, Lambda)
Responsibilities
Turn prototype models into fast, reliable services with well-defined API contracts; Build and orchestrate CI/CD pipelines; Review, refactor, optimize, containerize, deploy, and monitor data science models; Design batch/stream pipelines and online features linked to domain graphs; Build inference microservices with schema versioning and latency targets; Manage model/feature lifecycle including versioning and lineage; Instrument deep observability for traces, logs, metrics, and drift detection; Ensure real-time reliability through autoscaling, caching, and circuit breakers; Develop templates, SDKs, and documentation to standardize ML shipping.
Seniority
Staff, hands-on IC with strategic oversight
