AI/ML Engineer

Bain & Company • Austin, TX

95d • $79,500 - $95,250 • Hybrid

About The Position

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.

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, 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.
• 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.
• 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.
• 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.

Nice To Haves

• Exposure to model deployment, serving, or monitoring is a plus.
• Exposure to a modern cloud environment (Databricks, Azure, or AWS) is a plus.
• Exposure to LLMOps concepts: prompt versioning, model gateways (e.g., Portkey), and inference orchestration frameworks (LangChain, LlamaIndex, or equivalent).
• Exposure to LLM-as-judge evaluation patterns, even if applied under senior-engineer direction.

Responsibilities

• 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.
• 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.
• 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.

Benefits

• Annual discretionary performance bonus
• 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
• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Associate degree