Senior AI/ML Engineer, LLMOps & Production RAG
Overview
You will build and operate the deployment, serving, and LLMOps infrastructure for Bain & Co.'s private equity data products, taking models from prototype to governed production. You own the model and prompt lifecycle end-to-end, including packaging, registry, promotion, staged rollout, and rollback. You partner with Data Scientists on productionizing models and with Data Engineers on feature and embedding pipelines to serve ML and retrieval outputs into agent workflows. This hands-on role sets the standard for production ML systems, with a focus on measurable reliability and operational excellence. You will mentor mid-level engineers and drive incident response, making MLflow, Kubernetes, and AWS your daily toolkit.
What You'll Do10
- 1Build and deploy production inference services for models and embeddings using Python and MLflow, tuning batching and concurrency to meet latency and cost SLAs.
- 2Own the model and prompt lifecycle in MLflow, including packaging, registry governance, staged rollout behind feature flags, and rollback procedures.
- 3Maintain LLMOps tooling for prompt versioning and model gateway configuration, integrating Portkey or equivalent for inference orchestration and caching.
- 4Design and run production RAG pipelines with chunking, contextual embedding, hybrid retrieval, and cross-encoder re-ranking using pgvector or dedicated vector stores.
- 5Create evaluation frameworks with golden datasets and regression gates in CI, using LLM-as-judge with calibration to track drift and hallucination.
- 6Instrument production ML with Prometheus and OpenTelemetry for logs, metrics, and traces, setting up dashboards and alerts for latency and retrieval metrics.
- 7Collaborate with the Agent squad to serve structured tool responses, and with Data Engineers on feature pipelines, ensuring seamless integration.
- 8Lead incident response for production ML issues, treating deployment and maintenance as core delivery.
- 9Mentor mid-level engineers through code reviews and enforce engineering standards on pull requests.
- 10Use AI coding assistants to accelerate scaffolding but review all generated code against production standards before committing.
Requirements10
- 16+ years building and operating production ML systems, including deployment, serving, and monitoring.
- 2Bachelor's degree in Computer Science, Engineering, or related field, or equivalent practical experience.
- 3Strong Python for production ML with type hints, Pydantic, pytest, Ruff, and mypy strict.
- 4Experience with MLflow for experiment tracking, model registry, and custom model flavors.
- 5Docker and Kubernetes skills for containerizing workloads and writing Job and CronJob manifests.
- 6Experience with AWS or Databricks for managed training and serving.
- 7Ability to provision ML-serving infrastructure using Terraform including IAM roles and endpoints.
- 8Hands-on experience with pgvector or vector databases for embedding pipelines.
- 9Proven ability to implement evaluation frameworks with LLM-as-judge and regression gates in CI.
- 10Strong communication skills to explain trade-offs to technical and non-technical stakeholders.
Salary Insight
$141 - $169k per year
Location
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