Hiring: AI/ML Engineer Company: Bain & Company Location: Chicago, IL Job Posted Time: 2026-09-10 06:32:10 Employment Type: Full-time / Hybrid Target Skills & Keywords : AWS, Azure, Data Pipeline, Databricks, Docker, Git, LLM, LangChain, LlamaIndex, MLflow, Machine Learning, Model Registry, Python, RAG, TestNG, pgvector, pytest About the job Experience: •2+ years of experience building software, data, or ML systems, ideally including some exposure to production pipelines or services. •3 years of service and is 100% vested upon start date Required Skills: •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. Qualifications: •Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or a related field (or equivalent practical experience). •Exposure to model deployment, serving, or monitoring is a plus. •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. •Solid functional 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. •Operational 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. Compensation: •$72,000 - $86,500 / year •Competitive benefits and rewards package Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!