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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Engineers - **Company:** Bain & Company - **Location:** Atlanta, GA, United States - **Experience:** Experienced - **Salary:** $79,250.0 - $86,500.0 - **Contract:** Permanent contract - **Skills:** Adobe InDesign, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Engineering, Software Quality, Code Review, Python (Programming Language), Machine Learning, Software Construction, Management of Software Versions, Large Language Models, Generative AI, Git, Pytest, Information Technology, Machine Learning Operations, Celery, Docker, Databricks - **Published:** August 10, 2026 - **Apply:** https://careers.bain.com/jobs/Login?folderId=108806 ## About the Role * 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. ## Description * 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., * 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 ## Related Videos - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Celery on AWS ECS - the art of background tasks & continuous deployment](https://www.wearedevelopers.com/videos/561-celery-on-aws-ecs-the-art-of-background-tasks-continuous-deployment) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Walking into the era of Supply Chain Risks](https://www.wearedevelopers.com/videos/376-walking-into-the-era-of-supply-chain-risks) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)