Senior Machine Learning Engineer

About this role

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Skills you'll use

What you'll do

• 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.

Who can apply

• 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.

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