- Lead end-to-end generative AI and agentic AI initiatives from problem framing and experimentation through production release and measurable adoption.
- Design and evaluate foundation-model approaches, including model selection, fine-tuning strategies, retrieval and context design, and safety controls.
- Establish rigorous evaluation practices (quality, robustness, latency, cost, and risk) and translate results into clear recommendations and roadmaps.
- Build and operationalize production-grade machine learning and generative AI services using sound engineering practices, monitoring, and incident-ready controls.
- Define and drive semantic consistency across systems by leading semantic modeling standards and lifecycle governance in partnership with domain experts.
- Implement integration patterns that align data pipelines, application interfaces, and user experiences to reduce semantic conflicts and increase trust in outcomes.
- Partner with cross-functional leaders to define success metrics, manage trade-offs, and deliver outcomes aligned to business priorities and oversight expectations.
- Mentor and develop engineers and data scientists through technical leadership, reviews, and coaching to raise team capability and delivery velocity.
- Communicate complex technical concepts to both technical and non-technical audiences, influencing decisions through clarity, evidence, and pragmatism.
Required Qualifications, Capabilities, and Skills
- Master’s degree in computer science, engineering, statistics, or a related quantitative discipline.
- 7+ years of applied experience building and deploying machine learning and generative AI solutions in production environments.
- Strong proficiency in Python and modern machine learning frameworks (for example, PyTorch).
- Demonstrated ability to lead experimentation and evaluation, including defining metrics, running controlled comparisons, and documenting results for stakeholders.
- Experience deploying and operating models in production, including performance optimization, monitoring, and reliability trade-offs.
- Working knowledge of responsible AI practices, model risk concepts, and governance controls suitable for regulated environments.
- Strong software engineering fundamentals, including version control, code review, testing, and maintainable design.
- Proven ability to partner across product, engineering, and business stakeholders to translate ambiguous needs into clear outcomes and delivery plans.
Preferred Qualifications, Capabilities, and Skills
- PhD in a relevant quantitative field or demonstrated applied research impact in machine learning or generative AI.
- Publications, patents, or open-source contributions that demonstrate research depth and practical impact.
- Hands-on experience with transformer-based architectures, large-scale training or fine-tuning, and GPU-enabled development workflows.
- Experience building agentic AI systems (tool use, orchestration, planning, and workflow automation) with strong evaluation discipline.
- Familiarity with semantic modeling, ontologies, or semantic-layer design to improve consistency across analytics and AI use cases.
- Experience applying AI solutions within financial services or other highly regulated industries.