AI Engineer

Bain Capital is hiring an AI Engineer to join its Data Science & AI team in Boston, MA (onsite). This role focuses on turning AI into measurable business impact through end-to-end delivery, from gathering requirements and building datasets to deploying and optimizing AI systems in production.

What you’ll be working on

• Design, build, fine-tune, and deploy AI models and agentic applications for a range of business use cases.
• Partner with business users to capture requirements, explain technical approaches, and deliver actionable insights.
• Apply best practices to evaluate AI application performance, including accuracy, latency, scalability, and cost optimization.
• Create and maintain data processing pipelines that support high-quality labeled datasets for training and inference.
• Drive continuous improvement by staying current with AI research and innovations, exploring emerging methods, tools, and frameworks.
• Mentor junior engineers and data scientists, including leading internal training on AI best practices.

What you’ll bring

• Advanced Python proficiency for backend web application development and AI/ML model development.
• Experience deploying AI agents that can plan, reason, and execute complex tasks with minimal human intervention.
• Experience integrating search and vector databases (for example, Pinecone) to improve AI performance.
• Hands-on experience with AWS services such as EC2, EKS, S3, and Lambda, plus containerization (Docker, Kubernetes) and infrastructure-as-code (Terraform).
• Strong understanding of data workflows and distributed computing for large-scale data 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 environments.
• Track record of creating business value by applying machine learning to complex, real-world scenarios.
• Ability to operate independently in a dynamic environment, with comfort taking ownership and completing projects.
• Comfort with rapid iteration, strategy pivots, and learning new technologies as needed.
• Strong verbal and written communication skills to collaborate across technical and non-technical stakeholders.
• Proven ability to mentor others, lead complex initiatives, and foster collaboration.

Tools and technologies

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 machine learning, experiment tracking (MLflow, Weights & Biases), and model deployment.
• Traditional ML: experience deploying and optimizing models such as XGBoost and Scikit-Learn.
• Frontend: working knowledge of React or similar frameworks to build user-facing AI-driven applications.