AI/ML Engineer, Production Pipelines & RAG

Bain & Company; Dallas (TX); Hybrid; USD 83,000 - 91,000; Full time

Job summary

Bain & Company is seeking an experienced ML Engineer to contribute to PEG's data and software solutions for private equity clients. The role focuses on building and maintaining production ML pipelines, RAG components, and embedding workflows within a collaborative team.

You will work with Data Engineers, Data Scientists, and the AI squad to ship reliable code, improve data quality, and support evaluation datasets. A hybrid model with in-office presence is required.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or related field.
• 2+ years building software, data, or ML systems, with production pipelines exposure.
• Experience with model deployment, serving, or monitoring is a plus.
• Strong Python skills and cloud familiarity (Databricks, Azure, AWS).
• Ability to take scoped tasks to tested, reviewed implementations with limited supervision.

Responsibilities

• Implement and maintain components of production data and ML pipelines: ingestion jobs, feature and embedding pipelines, and Celery-based workers.
• Build and support RAG and retrieval stack components: embedding calls, indexing, and basic retrieval logic.
• Write production-quality Python with typing, tests, and linting; obtain code review approval before merge.
• Instrument pipelines with logs and metrics; contribute to dashboards and alerts.
• Collaborate with Data Engineers, Data Scientists, and AI squads on defined tasks in larger workstreams.
• Keep runbooks and documentation current; participate in design and code reviews.

Skills

Python

Data pipelines

ML engineering

Docker

Git

LangChain

LLMOps

Cloud exposure

Education

Bachelor's degree in CS/Engineering/ML/Data Science

Tools

Databricks

Azure/AWS

pgvector

Celery

Job description

Bain & Company is seeking an experienced ML Engineer to contribute to PEG's data and software solutions for private equity clients. The role focuses on building and maintaining production ML pipelines, RAG components, and embedding workflows within a collaborative team.

You will work with Data Engineers, Data Scientists, and the AI squad to ship reliable code, improve data quality, and support evaluation datasets. A hybrid model with in-office presence is required.