Description & RequirementsWHO YOU'LL WORK WITH As the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.
Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making.
The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.
WHERE YOU'LL FIT WITHIN THE TEAM AI/ML Engineers on the Diligence Platform build and maintain the data, feature, and retrieval pipelines that power production RAG and ML systems. You work under the guidance of Senior ML Engineers and the Engineering Manager to implement and operate components of the ingestion, embedding, and retrieval stack, ship well-tested production code, and grow your ownership of these systems over time. You partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined pieces of larger pipelines and RAG workstreams, and you build the habits, tooling fluency, and production judgment expected of a Senior ML Engineer. This is a hands-on, growth-oriented engineering role: you are expected to ship reliable, observable code from your first weeks, and to take on increasing ownership as your track record builds.
WHAT YOU'LL DO Core ML and Data Pipeline Engineering (65%) - Implement and maintain components of production data and ML pipelines: ingestion jobs, feature and embedding pipelines, and Celery-based workers, under the direction of senior engineers.
- Build and support pieces of the RAG and retrieval stack: chunking, embedding calls, indexing into pgvector, and basic retrieval and re-ranking logic, following established patterns.
- Write production-quality Python: type hints, tests, and linting to the team's standards, with code reviewed by senior engineers before merge.
- Instrument the pipelines and services you own with structured logs and metrics, and help build the dashboards and alerts that make issues visible.
- Reproduce, triage, and fix bugs in pipeline and serving code, escalating ambiguous or high-severity issues to senior engineers.
Collaboration and Support (25%) - Partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined tasks within larger pipeline, retrieval, and evaluation workstreams.
- Contribute test cases and sample data to evaluation harnesses and golden datasets, under the direction of senior engineers.
- Participate in design reviews and code reviews, both as reviewer and reviewee, building judgment about production trade-offs.
- Keep runbooks, READMEs, and pipeline documentation current as you build and change the systems you touch.
Other (10%): - Use AI coding assistants to accelerate scaffolding and boilerplate, and review generated code against team standards before committing.
- Use LLMs to draft documentation and status notes; validate and refine outputs before sharing them.
- Take on interviewing and hiring-loop participation as your experience grows.
ABOUT YOU - Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or a related field (or equivalent practical experience).
- 2+ years of experience building software, data, or ML systems, ideally including some exposure to production pipelines or services.
- Exposure to model deployment, serving, or monitoring is a plus.
- Experience working with structured feedback and code review, and a track record of improving code quality over time.
- Experience collaborating with Data Engineers, Data Scientists, or the Agent / AI squad to ship features that depend on retrieval or ML outputs.
- Comfort with Python as a primary language; exposure to a modern cloud environment (Databricks, Azure, or AWS) is a plus.
- Demonstrated ability to take a well-scoped task from specification to a tested, reviewed implementation with limited supervision.
ML engineering / LLMOps - Working knowledge of Python for data and ML workloads: type hints, Pydantic, pytest, Ruff, with production-quality pipeline and serving code that would pass a code review.
- Familiarity with MLflow concepts: experiment tracking, model registry, and promotion workflows.
- Exposure to LLMOps concepts: prompt versioning, model gateways (e.g., Portkey), and inference orchestration frameworks (LangChain, LlamaIndex, or equivalent).
- Good understanding of model-serving concepts: latency, throughput, and batching, even without direct production ownership yet.
- RAG pipeline building blocks: chunking strategies, embeddings, and vector stores such as pgvector; able to contribute to indexing and retrieval jobs under senior guidance.
- Understanding of model and pipeline evaluation basics: what a golden dataset is, and why regression gates matter in CI.
- Docker: comfortable containerising pipeline or serving code and running it locally for testing.
- Git: confident with PR-based workflows.
Generative AI and agentic systems - Contributes to inference and retrieval services that feed agent workflows as structured tool responses.
- Supports RAG quality work: helps build and run recall and precision checks against defined benchmarks.
- Exposure to LLM-as-judge evaluation patterns, even if applied under senior-engineer direction.
General - Treats testing, observability, and documentation as part of the job, not an afterthought, even on smaller tasks.
- Raises questions and surfaces uncertainty early rather than guessing silently on ambiguous requirements.
- Uses AI tooling to move faster, and reviews all generated code and documentation critically before it enters the codebase.
- Communicates clearly with teammates about progress, blockers, and trade-offs; asks for help early.
- This role follows a hybrid model, requiring in-office presence at least 1 day per week
U.S. COMPENSATION INFORMATION Compensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain's best in class benefits package (details listed below).
Some local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:
In Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $79,250 - $86,500
In Texas, the good-faith, reasonable annualized full-time salary range for this role is between $83,313 - $90,750
In Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $87,438 - $95,250
Placement within these ranges will vary based on factors such as experience, education, training, and skill level.
Compensation also includes a discretionary annual performance bonus, 401(k) plan with employer contribution, and Bain's best-in-class benefits-including full premium coverage for medical, dental, and vision, generous paid time off, and more.
Annual discretionary performance bonus
This role may also be eligible for other elements of discretionary compensation
4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date
Bain & Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.
Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheck
Generous paid time off, including parental leave, sick leave and paid holidays
Fully vested 401(k) company contribution
Paid Life and Long-Term Disability insurance
Annual fitness reimbursements
