Team Leadership:
Lead and manage an established team of 10 or more AI Engineers, AI Architects, ML/Data Scientists, and Optimization Scientists.
Provide technical direction, coaching, performance management, and career development for team members.
Establish clear ownership, delivery expectations, and engineering standards across the team.
Promote collaboration across AI engineering, data science, architecture, optimization, cloud, platform, and business teams.
Identify capability gaps and support the continuous development of the team.
Project and Delivery Oversight:
Maintain close oversight of critical and high-impact AI projects from solution definition through production deployment.
Regularly review project status, technical risks, dependencies, resource constraints, and delivery commitments.
Proactively identify projects that are at risk and work with project leads to define corrective actions.
Ensure that AI initiatives deliver measurable business outcomes and are not limited to prototypes or proof-of-concept implementations.
Communicate delivery status, risks, decisions, and escalations clearly to senior stakeholders.
Balance priorities and resources across multiple concurrent AI, ML, GenAI, and optimization initiatives.
Technical and Architectural Leadership:
Provide hands-on technical guidance for complex AI, machine learning, optimization, and Generative AI solutions.
Review and challenge solution architectures, technical designs, implementation approaches, and technology selections.
Architect scalable, secure, cost-effective, and production-ready AI solutions across Microsoft Azure and/or AWS.
Ensure appropriate integration between AI solutions and enterprise applications, APIs, data platforms, cloud services, and operational systems.
Guide teams on software engineering practices, including modular design, testing, version control, CI/CD, observability, reliability, and maintainability.
Ensure solutions meet enterprise requirements for security, privacy, governance, compliance, performance, and responsible AI.
Generative and Agentic AI:
Lead the design and implementation of enterprise Generative AI and Agentic AI solutions.
Guide teams on areas such as Retrieval-Augmented Generation, tool-calling agents, multi-agent workflows, prompt engineering, model routing, and AI orchestration.
Establish appropriate evaluation frameworks for GenAI and Agentic AI solutions, including accuracy, groundedness, relevance, safety, latency, reliability, and cost.
Ensure that GenAI applications include appropriate guardrails, human oversight, monitoring, and fallback mechanisms.
Evaluate emerging AI technologies and determine their suitability for enterprise use cases.
Business and Stakeholder Engagement:
Translate complex business problems into clear AI, ML, optimization, and data-driven solution approaches.
Work with business leaders to define use cases, expected outcomes, success measures, and adoption plans.
Communicate technical concepts, architectural decisions, trade-offs, risks, and limitations to both technical and non-technical stakeholders.
Ensure alignment between business priorities, technical feasibility, delivery capacity, and enterprise strategy.
Support the adoption and operationalization of AI solutions across the organization.
