Lead, AI & Machine Leaning Engineering

WHAT YOU’LL DO

• Design, build, and deliver statistical/ML models and agentic AI/LLM solutions to solve high-impact client problems.
• Architect and implement production-grade ML systems by building enterprise-grade services, applying MLOps and CI/CD for reliable deployment and monitoring, and ensuring high standards in engineering quality and documentation.
• Partner with software teams and client stakeholders to translate business problems into ML solutions, prioritize work, and enable enterprise‑scale AI transformation.
• Continuously research emerging frameworks and state‑of‑the‑art AI (e.g., LLMs, agents, retrieval, post-training, etc.) and convert learnings into reusable frameworks, components, and accelerators used across industries and functions.
• Share knowledge and mentor others on best practices tools, and approaches in applied machine learning and AI engineering.
• Contribute to internal R&D and learning initiatives to advance Bain’s AI capabilities and foster continuous innovation between projects.
• Participate in hiring of other data science team members.
• Support individual team members and provide technical input and mentorship to help the team deliver current work in scope. Assess and help them improve their engineering practices.
• Advise business and technology partners on technology solutions leveraging emerging technologies and on mitigation of disruption risk.

Knowledge, Skills & Abilities:

• Programming & Data Processing: Python, SQL, PySpark, Pandas, Apache Spark, Databricks, Delta Lake
• Machine Learning Frameworks: TensorFlow, scikit-learn, PyTorch, XGBoost, LightGBM
• MLOps & Model Lifecycle: MLflow, Kubeflow, Azure Machine Learning, Feature Stores, experiment tracking, automated retraining pipelines
• Cloud & Infrastructure: Azure ML, Azure Databricks, Synapse, ADLS, AKS, Azure Functions
• Model Deployment & Scaling: Dockerized models, REST/GRPC serving, AKS real-time inference, batch scoring, distributed/GPU training
• DevOps & CI/CD: Git, GitHub Actions, Azure DevOps, Terraform, automated ML pipeline deployments

Experience:

• 8-10 years of relevant experience; Advanced degree preferred.
• Strong theoretical and practical experience in machine learning
• Proficiency in Python, with solid software engineering foundations — including clean code practices, testing, version control, and CI/CD workflows.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and deploying ML/AI applications.
• Comprehensive understanding of the ML lifecycle, from experimentation and model development to deployment.
• Experience productionizing and exposing ML models (e.g., using FastAPI, Flask, or similar frameworks) for seamless integration into applications and platforms.
• Excellent communicator and collaborator, able to align with technical and business stakeholders in a fast-paced, cross-functional environment.
• Curious and growth-oriented, passionate about exploring new tools, mentoring peers, and sharing best practices.
• Adaptable and self-driven, comfortable operating with autonomy, embracing feedback, and learning new skills to deliver client impact.
• Strong communication, time management and customer service skills
  • High performance and standards as demonstrated by academic and previous job experience.