About the role
Requirements:
- 13 - 18 years in ML engineering/MLOps or backend 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, and testing.
- Hands-on with orchestration frameworks and libraries (LangChain, LlamaIndex, OpenAI Function Calling, AgentKit, etc. ).
- Knowledge of agent architectures (reactive, planning, and 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, and 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, products, and the data and AI platform; you document and teach.
- Pragmatic: You automate the 80% and leave room for rapid experimentation.