AI Engineer

Bain Capital is expanding its Data Science & AI team in Boston, MA (onsite) and hiring an AI Engineer to develop and deploy AI solutions at scale. This role blends hands-on model and agent engineering with practical delivery, from requirements and data through production performance optimization, in partnership with Investment teams.

What you’ll build

You will design, build, fine-tune, and deploy AI models and agentic applications for a range of business use cases. The work includes collaborating with business users to define requirements, translating them into technical approaches, and delivering actionable insights. You will also improve AI applications by implementing strong evaluation practices across accuracy, latency, scalability, and cost.

• Develop and fine-tune AI models and agentic applications for business needs
• Partner with business users to gather requirements and communicate technical plans
• Evaluate and optimize AI applications for accuracy, latency, scalability, and cost
• Build robust data processing pipelines to support high-quality labeled datasets for training and inference
• Stay current on AI research and apply emerging methods, tools, and frameworks

Engineering impact from data to production

The role emphasizes end-to-end delivery and operational readiness. You will work with large-scale data workflows and distributed computing to support ingestion, preprocessing, and feature engineering. You will also establish and use production-grade infrastructure practices involving containers, AWS services, and infrastructure-as-code.

Collaboration and growth

Bain Capital values knowledge sharing. You will provide guidance to junior engineers and data scientists and lead internal training sessions focused on AI best practices.

Requirements

• Advanced Python proficiency for backend engineering of web applications and AI/ML development
• Experience deploying AI agents that can plan, reason, and execute complex tasks with minimal human intervention
• Experience integrating search and vector databases (e.g., Pinecone) to improve AI performance
• Experience with AWS (e.g., EC2, EKS, S3, Lambda), containerization (Docker, Kubernetes), and infrastructure-as-code (Terraform)
• Strong understanding of data workflows and distributed computing for large-scale ingestion, preprocessing, and feature engineering
• BS or MS in Computer Science, Data Science, Machine Learning, or a related technical field
• Several years of hands-on experience building and deploying machine learning or NLP solutions in production
• Demonstrated ability to create business value using machine learning on real-world scenarios
• Ability to operate independently, take ownership, and drive projects to completion in a dynamic environment
• Comfort with rapid iteration, strategy pivots, and adopting new technologies
• Strong verbal and written communication skills for technical and non-technical stakeholders
• Proven ability to mentor junior team members and lead complex initiatives

Technologies you’ll work with

• Python, AWS (EC2, EKS, S3, Lambda)
• Docker, Kubernetes
• Terraform
• Pinecone
• CI/CD, MLflow, Weights & Biases
• XGBoost, Scikit-Learn
• React

Nice to have

• MLOps tools: familiarity with CI/CD for ML, experiment tracking (MLflow, Weights & Biases), and model deployment
• Traditional ML: experience deploying and optimizing models such as XGBoost and Scikit-Learn
• Frontend skills: working knowledge of React or similar frameworks for user-facing AI-driven applications