Senior AI/ML Engineering Manager, LLMOps & RAG
Overview
Lead the Data and ML Engineering function for Bain's Diligence Platform, owning teams that build Python-based feature and embedding pipelines, MLflow model serving, and LLMOps infrastructure. Manage a team of data and ML engineers while setting architectural direction for production RAG and retrieval systems. Partner with Data Science and Product leadership to translate strategy into measurable engineering work. This player-coach role offers the chance to shape the platform's technical standards and mentor senior engineers.
What You'll Do8
- 1Manage a team of Data and ML Engineers: hire, onboard, coach, and develop careers while setting engineering standards for Python and ML systems.
- 2Own delivery of the team's roadmap: scope work, set commitments, track progress, and remove blockers across pipeline, serving, retrieval, and evaluation workstreams.
- 3Set architectural direction for data and serving systems, lead design reviews, and prototype hard problems in LLMOps and RAG pipelines.
- 4Contribute to production code in inference and serving systems, model and prompt lifecycle in MLflow, and RAG retrieval systems.
- 5Own the evaluation and monitoring bar for production ML: golden datasets, metric definitions, regression gates in CI, and drift and hallucination monitoring.
- 6Lead incident response on highest-severity production ML and data issues and conduct post-incident reviews.
- 7Grow senior engineers into technical leaders by delegating architectural ownership and coaching through design review.
- 8Communicate roadmap, trade-offs, risks, and delivery status to platform leadership and non-technical stakeholders.
Requirements10
- 18+ years building and operating production data and ML systems, including model deployment, serving, and post-deployment monitoring.
- 23+ years managing data, ML, or platform engineers, including hiring, performance management, and career development.
- 3Strong Python for production ML, with type hints, Pydantic, pytest, Ruff, and mypy strict.
- 4Experience with MLflow for experiment tracking, model registry, custom flavours, and serving configuration.
- 5Experience with LLMOps tooling: prompt versioning, model gateways, inference orchestration frameworks like LangChain or LlamaIndex.
- 6Experience building and operating production RAG systems end-to-end, from embedding and retrieval to re-ranking and evaluation.
- 7Experience with Docker and Kubernetes in ML serving infrastructure, including Terraform-provisioned resources.
- 8Experience partnering with Data Science, Product, and platform leadership to translate strategy into sequenced engineering work.
- 9Experience owning the model and prompt lifecycle end-to-end, including packaging, registry, promotion, rollout, monitoring, and rollback.
- 10Bachelor's degree in Computer Science, Engineering, or related field, or equivalent practical experience.
Salary Insight
$148 - $178k per year
Location
Required Skills
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