Senior AI/ML Engineer, LLMOps & RAG Systems
Overview
You build production inference, serving, and LLMOps infrastructure for Python-based ML systems, taking models from prototype to governed deployment. Bain's Private Equity Group Innovation team creates proprietary data and software products, and your work supports over 1,000 professionals across the investment lifecycle. You collaborate with Data Scientists, Data Engineers, and the Agent/AI squad to deliver reliable, observable ML systems. This hands-on role sets engineering standards and mentors mid-level engineers, with daily use of MLflow, Kubernetes, and Databricks.
What You'll Do10
- 1Build and operate production inference services with request batching, concurrency, and throughput tuning against latency and cost SLAs using Python and Docker.
- 2Own the model and prompt lifecycle in MLflow: packaging, registry governance, promotion behind feature flags, and clean rollback.
- 3Maintain LLMOps tooling with prompt versioning, model-gateway configuration like Portkey, inference orchestration, and response caching.
- 4Design and operate production RAG pipelines: structure-aware chunking, contextual embedding, hybrid retrieval with pgvector, and cross-encoder re-ranking.
- 5Build evaluation frameworks with golden datasets, LLM-as-judge calibration, and regression gates in CI.
- 6Instrument production systems with structured logs, OpenTelemetry spans, and Prometheus metrics for token usage and drift.
- 7Serve model and retrieval outputs as structured tool responses for the Agent Gateway, partnering on feature pipelines.
- 8Drive incident response to resolution, treating deployment and monitoring as core delivery.
- 9Mentor mid-level engineers on production ML practices and enforce standards through code reviews.
- 10Use AI coding assistants to accelerate development while reviewing all generated code and documentation critically.
Requirements10
- 16+ years building and operating production ML systems with model deployment and monitoring.
- 2Proven ownership of model and prompt lifecycle end-to-end using MLflow.
- 3Proven experience building RAG or retrieval systems end-to-end with pgvector or similar vector stores.
- 4Cross-functional collaboration with Data Science, Data Engineering, and Product teams to ship ML capabilities.
- 5Strong Python skills with type hints, Pydantic, pytest, Ruff, and mypy strict mode.
- 6Experience in cloud ML environments like Databricks or AWS, including managed training and serving.
- 7Demonstrated mentorship and raising engineering standards through code review and repository conventions.
- 8Familiarity with Docker and Kubernetes for containerizing inference workloads.
- 9Experience with Terraform for provisioning ML infrastructure like IAM roles and model endpoints.
- 10Ability to design feedback-to-evaluation loops using production signals and LLM-as-judge patterns.
Salary Insight
$141 - $169k per year
Location
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