This role demands a seasoned leader with deep expertise in steering AI/ML-driven product development, bridging technical execution with business strategy to drive measurable impact. The ideal candidate thrives at the intersection of product vision and technical leadership, ensuring alignment across teams while optimizing resource allocation for high-impact outcomes.
With at least 8 years of experience in technical product management, engineering management, or similar roles leading technical teams in AI/ML or data-driven product development Key Responsibilities:
Set strategic priorities and determine team focus across AI Enablement and AI Experiments tracks, ensuring measurable progress toward organizational goals
Serve as the primary liaison with internal business teams to understand workflows, gather requirements, and translate business pain points into actionable technical work
Collaborate with product teams to align exploration and experimentation efforts with broader product direction
Lead the team’s operating rhythm, including stand-ups, demos, planning sessions, and progress readouts to leadership and stakeholders
Allocate resources across workstreams, moving team members based on shifting priorities to maximize impact and efficiency
Evaluate and shut down experiments or projects that are not delivering results, reprioritizing efforts swiftly and effectively
Guide the team’s technology roadmap by making decisions on model selection, infrastructure, build-vs-buy tradeoffs, and adoption of new tools
Define and evolve AI governance and compliance practices, establishing guardrails for responsible AI use, data handling, and decision explainability
Manage and optimize AI infrastructure spend, tracking LLM costs, token usage patterns, and vendor contracts to ensure cost-effective operations
Required Skills:
Advanced proficiency in Python for code review, scripting, and prototyping
Strong understanding of LLM-based systems, including retrieval-augmented generation pipelines
Experience with ML frameworks such as TensorFlow, PyTorch, and Sci-Kit Learn
Hands-on experience with Azure cloud infrastructure for deploying, monitoring, and scaling AI workloads
Expertise in statistical analysis and computing, including hypothesis testing, t-test, z-test, and regression techniques
Proficiency in forecasting techniques including exponential smoothing, ARIMA, and ARIMAX
Knowledge of classification algorithms such as decision trees and SVM
Familiarity with tools like KubeFlow and BentoML for ML lifecycle management
Understanding of probabilistic graph models and advanced distance metrics (Hamming, Euclidean, Manhattan)
Preferred Skills:
Experience with agent orchestration patterns for multi-step AI workflows
Expertise in prompt engineering to optimize output quality in LLM-based systems
Proficiency with Great Expectations and Evidently AI for data validation and monitoring
Experience defining AI governance frameworks for compliance and responsible data handling
Desired Qualifications:
Bachelor's degree in Computer Science, Data Science, Statistics, Information Technology, or a closely related discipline
Certification in Machine Learning, Data Science, or Artificial Intelligence from a recognized institution
Certification in Azure AI or Cloud Services (such as Microsoft Certified: Azure AI Engineer Associate)
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