Machine Learning Engineer

Bain Capital • Boston, MA

1d

About The Position

We are seeking a hands-on Machine Learning Engineer (MLE) to build and productionize data and machine learning systems that support investment decision-making at Bain Capital. You will be responsible for the full pipeline, from ingesting raw data to serving real-time or batch predictions, and will work closely with data scientists, data engineers, and investment professionals. The ideal candidate will demonstrate strong software engineering practices, expertise in MLOps, and the ability to communicate effectively with both technical and non-technical stakeholders.

Requirements

• Fluency in Python and SQL, with strong fundamentals in software and data engineering.
• Hands-on experience with Airflow, Snowflake, dbt, and Docker-based containerization or similar tools.
• Proven experience deploying machine learning models to the cloud (preferably AWS) using CI/CD pipelines (e.g., GitHub Actions), infrastructure-as-code tools (e.g., Terraform), and container orchestration platforms (e.g., Kubernetes).
• Practical knowledge of experiment tracking (MLflow or W&B) and observability and data quality stacks (Datadog, Monte Carlo, Great Expectations or equivalents).
• Ability to communicate complex technical concepts clearly to business stakeholders.
• Proficiency with Python machine learning libraries (e.g., scikit-learn, XGBoost) and at least one deep learning framework (e.g., PyTorch or TensorFlow).
• Bachelor’s degree with relevant experience or Master’s degree in Computer Science, Data Science, or a related field.
• 3+ years designing, building, and operating production ML systems.

Nice To Haves

• Familiarity with vector databases (e.g., Pinecone) and RAG architectures.
• Exposure to real-time or streaming data systems (Kafka, Kinesis) and distributed compute frameworks (Spark, Dask).
• Experience in financial services or other high-stakes decision-support environments.
• Working knowledge of React or similar front-end frameworks to support interactive data science applications.

Responsibilities

• Data Engineering: Design, implement, and maintain scalable, well-tested data pipelines using technologies such as Snowflake, dbt, Airflow, and Monte Carlo.
• Model Lifecycle Management: Train, package, deploy, and continuously retrain machine learning models. Track experiments using tools such as MLflow or Weights & Biases.
• Serving & DevOps: Containerize services with Docker, expose inference via FastAPI, and operate workloads on AWS infrastructure managed through Terraform.
• Monitoring & Observability: Instrument pipelines and models to detect data drift, performance regressions, and SLA breaches using DataDog and tools like Evidently.
• Generative AI Enablement: Prototype RAG pipelines that pair LLMs with vector databases like Pinecone. Guide prompt-engineering and evaluation best practices.
• Collaboration & Knowledge Sharing: Translate investment team requirements into technical solutions, document system architecture and runbooks, and mentor team members on machine learning engineering best practices.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 501-1,000 employees