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About the Role
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. This is a hands-on, growth-oriented engineering role where you are expected to ship reliable, observable code from your first weeks and take on increasing ownership as your track record builds.
Responsibilities
- Implement and maintain components of production data and ML pipelines: ingestion jobs, feature and embedding pipelines, and Celery-based workers.
- Build and support pieces of the RAG and retrieval stack: chunking, embedding calls, indexing into pgvector, and basic retrieval and re-ranking logic.
- Write production-quality Python code with type hints, tests, and linting to team standards.
- Instrument owned pipelines and services with structured logs and metrics.
- Build dashboards and alerts to make issues visible.
- Reproduce, triage, and fix bugs in pipeline and serving code.
- Escalate ambiguous or high-severity issues to senior engineers.
- 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.
- Participate in design reviews and code reviews.
- Keep runbooks, READMEs, and pipeline documentation current.
- Use AI coding assistants to accelerate scaffolding and boilerplate.
- Review AI-generated code against team standards before committing.
- Use LLMs to draft documentation and status notes.
- Validate and refine LLM-generated outputs before sharing.
- Take on interviewing and hiring-loop participation as experience grows.
Requirements
- 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.
- 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.
- Working knowledge of Python for data and ML workloads: type hints, Pydantic, pytest, Ruff, with production-quality pipeline and serving code.
- 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.
- RAG pipeline building blocks: chunking strategies, embeddings, and vector stores such as pgvector.
- 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.
- 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.
- Treats testing, observability, and documentation as part of the job.
- Raises questions and surfaces uncertainty early.
- Uses AI tooling to move faster.
- Communicates clearly with teammates about progress, blockers, and trade-offs.
- Asks for help early.
Skills
- Python
- type hints
- Pydantic
- pytest
- Ruff
- MLflow
- LLMOps
- LangChain
- LlamaIndex
- Docker
- Git
- RAG
- pgvector
- Generative AI
- Agentic systems
Location
- Atlanta
- Austin
- Chicago
- Dallas
- Houston
Work Type
- Permanent Full-Time
- Hybrid
Experience Level
- 2+ years of experience building software, data, or ML systems
- Senior ML Engineer (guidance)
Education Level
- Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or a related field (or equivalent practical experience)
Salary/Compensations
- Atlanta: $72,000 - $86,500
- Texas: $75,750 - $90,750
- Chicago: $79,500 - $95,250
Benefits
- Annual discretionary performance bonus
- 401(k) plan with an annual employer contribution
- 4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date
- Full premium coverage for medical, dental, and vision programs
- Generous paid time off, including parental leave, sick leave and paid holidays
- Paid Life and Long-Term Disability insurance
About the Company
- We are proud to be consistently recognized as one of the world’s best places to work.
- We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.
- Extraordinary teams are at the heart of our business strategy.
- We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.
- 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.