AI/ML Engineer, RAG & Production Pipelines | Bain
Overview
You will own the data, feature, and retrieval pipelines that power production RAG and ML systems for Bain's Private Equity Group. Working with Senior ML Engineers and the Engineering Manager, you will implement and operate ingestion, embedding, and retrieval components, shipping reliable, observable code from day one. You will partner with Data Engineers and Data Scientists on larger pipeline and RAG workstreams, building habits and judgment for Senior ML Engineer growth. This role offers hands-on ownership growth and exposure to cutting-edge AI solutions in private equity.
What You'll Do10
- 1Implement and maintain production data and ML pipelines: ingestion jobs, feature and embedding pipelines, Celery-based workers, under senior engineer guidance.
- 2Build and support RAG and retrieval stack components: chunking, embedding calls, indexing into pgvector, and basic retrieval and re-ranking logic.
- 3Write production-quality Python with type hints, tests, and linting; review code with senior engineers before merge.
- 4Instrument pipelines and services with structured logs and metrics; build dashboards and alerts for visibility.
- 5Reproduce, triage, and fix bugs in pipeline and serving code, escalating high-severity issues promptly.
- 6Partner with Data Engineers, Data Scientists, and the Agent / AI squad on pipeline, retrieval, and evaluation tasks.
- 7Contribute test cases and sample data to evaluation harnesses and golden datasets under senior direction.
- 8Participate in design and code reviews, building judgment on production trade-offs.
- 9Keep runbooks, READMEs, and pipeline documentation current as you build and change systems.
- 10Use AI coding assistants and LLMs to accelerate scaffolding and documentation, reviewing all generated outputs critically.
Requirements10
- 12+ years building software, data, or ML systems, with exposure to production pipelines or services.
- 2Bachelor's degree in Computer Science, Engineering, ML, Data Science, Statistics, or equivalent practical experience.
- 3Working knowledge of Python for data and ML workloads: type hints, Pydantic, pytest, Ruff.
- 4Familiarity with MLflow concepts: experiment tracking, model registry, promotion workflows.
- 5Exposure to LLMOps concepts: prompt versioning, model gateways (e.g., Portkey), inference orchestration (LangChain, LlamaIndex).
- 6Understanding of RAG pipeline blocks: chunking, embeddings, vector stores like pgvector.
- 7Comfortable containerizing pipeline or serving code with Docker and testing locally.
- 8Confident with Git PR-based workflows.
- 9Exposure to model-serving concepts: latency, throughput, batching.
- 10Understanding of model evaluation basics: golden datasets and regression gates in CI.
Salary Insight
$79 - $95k per year
Location
Required Skills
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