ML Engineer

Requirements:

• 6 - 9 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, product, and the data and AI platform; you document and teach.
• Pragmatic: You automate the 80% and leave room for rapid experimentation.