Director, AI and Machine Learning Engineering

As Director, you lead the AI and ML engineering vision for Bain’s Growth AI Performance System. You own core agentic capabilities and scalable production-grade AI workflows that unlock enterprise decisioning at scale. You’ll collaborate with cross-functional teams, shaping architecture and product direction while balancing quality, cost, and security. The role blends research and production engineering with direct client engagement, delivering tangible business impact. This is a hands-on leadership role at the intersection of innovation and execution, offering early ownership within a global firm.

• comprehensive benefits package
• medical, dental and vision insurance
• generous paid time off including parental leave
• fully vested 401(k) company contribution
• life and long-term disability insurance
• competitive compensation packages

• Develop and own the Growth AI Performance System Agentic Core and orchestration Frameworks
• Design and implement end-to-end AI workflows from POC to production at enterprise scale
• Build robust retrieval and knowledge pipelines across vector, keyword, hybrid, and graph data
• Apply fine-tuning and PE techniques and drive model experimentation
• Ensure secure, compliant deployment with governance and HITL where appropriate
• Engineer for cloud-native deployment, CI/CD, observability, and reproducibility
• Translate stakeholder feedback into engineering improvements and communicate design decisions to leadership
• Mentor engineers on RAG, agents, and AI safety
• Collaborate with product, data science, ethics, and infrastructure teams to deliver robust AI systems
• Drive innovation through POCs and adoption of new tools and capabilities
• Maintain high-quality software practices across the full development lifecycle

• 10+ years in software engineering or applied AI roles with hands-on building responsibilities
• Production AI/ML shipping experience from experimentation to live deployment
• Strong Python and production services experience; hands-on with LLM APIs and agentic orchestration
• Experience designing agentic workflows, memory/state management, tool integration, and RH/L routing
• Knowledge of retrieval pipelines across vector/graph data; embedding optimization and indexing
• MLOps/AIOps practices including CI/CD, versioning, evaluative pipelines, observability
• Advanced prompt engineering and context engineering; evaluation design and labeling
• Solid system design skills with cost optimization for LLM workloads
• Data science skills for feature engineering, model selection, training, validation
• Client-facing experience and ability to present to senior stakeholders
• Security, privacy, governance, and responsible AI practices

• strategic storytelling to non-technical audiences
• strong collaboration across cross-functional teams
• mentorship and leadership
• Python
• LLM APIs
• agentic orchestration frameworks