Senior ML Systems Engineer – Production & RAG Ops

Bain & Company; Austin (TX); Hybrid; USD 148,000 - 162,000; Full time

Job summary

Bain & Company is seeking a Senior ML Engineer to own the lifecycle of production ML systems, including deployment, serving, and monitoring. You will lead LLMOps tooling, RAG pipelines, and model evaluation while mentoring engineers and collaborating with Data Scientists and Data Engineers.

The role emphasizes a hybrid model with in-office presence at least one day per week in the U.S., and a market-competitive compensation package.

Qualifications

• Bachelor’s degree or higher in a relevant field.
• 6+ years building and operating production ML systems.
• Experience owning model and prompt lifecycle end-to-end.
• Experience building RAG/retrieval systems end-to-end.
• Strong collaboration with Data Science, Data Engineering, and Product teams.
• Proficient in Python for production ML (typing, tests).
• Experience with cloud ML platforms (Databricks or AWS).
• Mentor engineers and enforce coding standards.

Responsibilities

• Build, deploy, and operate production inference and serving systems for models, embeddings, and re-rankers.
• Own the model and prompt lifecycle in MLflow including registry governance and rollout.
• Develop LLMOps tooling for prompts, model gateways, and response caching.
• Build production RAG and retrieval pipelines end-to-end.
• Design and maintain model evaluation frameworks and monitoring.
• Instrument ML systems with logs, traces, and metrics; ensure SLA adherence.
• Collaborate with Agent/AI squads to integrate outputs into workflows.
• Lead production ML incident response and maintain system reliability.
• Mentor engineers and set engineering standards for ML.

Skills

Python production

ML systems

LLMOps

Team collaboration

Model deployment

Education

Bachelor’s degree in Computer Science, Engineering, ML, Data Science, Statistics, or related field

Tools

Databricks

AWS

Docker

Kubernetes

MLflow

Job description

Bain & Company is seeking a Senior ML Engineer to own the lifecycle of production ML systems, including deployment, serving, and monitoring. You will lead LLMOps tooling, RAG pipelines, and model evaluation while mentoring engineers and collaborating with Data Scientists and Data Engineers.

The role emphasizes a hybrid model with in-office presence at least one day per week in the U.S., and a market-competitive compensation package.