Machine Learning/GenAI Engineering Manager

This role is eligible for our hybrid work model: 2 days in-office This job posting is for an existing, currently vacant position. Machine Learning /GenAI Engineering Manager Our Technology team is the backbone of our company: constantly creating, testing, learning and iterating to better meet the needs of our customers. If you thrive in a fast-paced, ideas-led environment, you’re in the right place. Why this job’s a big deal:  We are looking for an ML/GenAI Engineering Manager to lead and grow a high-performing team of 7 ML Platform and MLOps developers. In this role, you will lead the creation and scaling of a unified, centralized ML and GenAI platform used by data scientists across various product teams. You will drive the architecture and delivery of core infrastructure supporting both traditional ML model training/deployment and cutting-edge GenAI applications, while owning key shared tools like evaluation, monitoring, MCP integration, and LLM gateways. In this role, you will get to: People & Team Leadership: Lead, mentor, and advocate for a hybrid team of 7 ML Engineers and MLOps developers. Set clear goals, manage performance, and foster a strong culture of technical excellence and continuous learning. Platform Vision & Strategy: Drive the roadmap for a centralized, self-service AI/ML platform that simplifies model training, evaluation, deployment, and monitoring across all product lines. GenAI Infrastructure & Tooling: Own and standardize enterprise shared AI development tooling, including model evaluation frameworks, observability, safety monitoring, MCP (Model Context Protocol), and LLM gateway integrations. Cross-Functional Collaboration: Partner closely with data science leads, product managers, security teams, and engineering leaders to align platform capabilities with business needs and technical standards. Operational Excellence: Establish robust MLOps and LLMOps best practices, ensuring high availability, scalable infrastructure, cost optimization, and enterprise-grade security for all deployed models and APIs.

Education: Bachelor’s degree in Computer Science, Software Engineering, or a closely related quantitative field (or a Master’s degree in a related technical discipline). Leadership Experience: 3+ years of experience in technical engineering management or direct team leadership roles within ML, Data Platform, or Infrastructure engineering. Hands-on Platform Experience: 5+ years of hands-on experience building, scaling, and maintaining ML platforms, MLOps pipelines, or cloud-based data systems. ML & GenAI Domain Knowledge: Proven track record leading projects that support both traditional machine learning lifecycles (experiments, training, registry, inference) and modern GenAI architectures (RAG, fine-tuning, orchestration, LLM gateways). Infrastructure & Cloud: Strong technical background in cloud platforms (GCP preferred), containerization (Kubernetes, Docker), and infrastructure automation (Terraform). Tooling & Observability: Demonstrated experience establishing developer-facing tooling, monitoring, and evaluation frameworks for AI workloads. Communication: Excellent communication skills with the ability to bridge technical concepts between data scientists, platform engineers, and executive leadership.

Advanced degree (Master's or Ph.D.) in Computer Science, Machine Learning, Data Science, or Artificial Intelligence. Experience designing or evaluating emerging GenAI standards, such as MCP, modern agentic frameworks, or API gateway patterns for LLMs. Background in managing mixed-skill technical teams across both application engineering and core platform infrastructure. Familiarity with data security, privacy compliance, and cost governance in large-scale cloud AI deployments. There are a variety of factors that go into determining a salary range, including but not limited to external market benchmark data, geographic location, and years of