AI Platform Adoption & Enablement Lead

Job Description

The AI Platform Adoption & Enablement Lead is a senior individual contributor responsible for accelerating enterprise adoption of AI by guiding how AI applications are designed, built, deployed, and monitored. This role provides deep technical expertise across the AI lifecycle, with a strong focus on application development patterns, observability, and operational excellence. The leader partners across engineering, product, platform, and business teams to ensure AI use cases are implemented using scalable, secure, and enterprise‑aligned approaches.

The AI Platform Adoption & Enablement Lead serves as a hands‑on expert and enterprise advisor, helping teams successfully implement AI use cases while ensuring consistency with platform standards and long‑term maintainability. This role is accountable for shaping how AI applications are built and operated, with particular emphasis on:

• AI application design and development patterns
• Observability, monitoring, and production performance
• Practical adoption of AI platforms, tools, and services
• Scalable implementation of enterprise AI use cases

• Provide hands‑on guidance on how to design, build, and deploy AI applications across a variety of use cases (ML, GenAI, agentic AI).
• Define and operationalize observability and monitoring frameworks for AI systems, including performance, drift, reliability, and usage tracking.
• Guide teams in implementing production‑ready AI solutions, ensuring scalability, resiliency, and compliance with enterprise standards.
• Partner with product and engineering teams to shape and refine AI use cases, balancing feasibility, value, and technical complexity.
• Drive adoption of enterprise AI platforms, tools, and services through practical enablement and technical advisory.
• Establish and document best practices, patterns, and reusable approaches for AI application development and deployment.
• Support teams in evaluating and selecting appropriate models, architectures, and tools based on use case requirements.
• Ensure AI implementations align with enterprise expectations for security, risk, and governance.

• Provide expert guidance on building AI applications end‑to‑end, including prompt design, orchestration, model integration, and API‑based deployment.
• Advise on architectural patterns for different categories of AI solutions (predictive ML, GenAI, agent‑based systems).
• Partner with teams to translate business problems into scalable AI solutions.

• Lead enterprise MLOps practices, including CI/CD, model registry, and automated rollback.
• Ensure AI systems meet regulatory, security, and privacy standards in collaboration with risk and compliance stakeholders.
• Define and review SLAs, KPIs, and observability standards for AI services, ensuring operational excellence and accountability.

• Set the architectural vision for a scalable, modular AI ecosystem spanning data ingestion, feature stores, training infrastructure, and inference.
• Champion standards for model governance, including versioning, data lineage, explainability, and auditability.
• Evaluate emerging AI technologies and approaches, and define adoption roadmaps aligned with business value and risk tolerance.

• Own the strategic evolution of the on‑prem AI platform stack (e.g., Airflow, Elasticsearch) and its operating model.
• Ensure delivery of self‑service platforms, APIs, and tooling that enable teams to innovate efficiently and safely.
• Partner with cloud, Dev Ops, and security leaders to balance performance, cost efficiency, scalability, and compliance.

• Define and standardize observability frameworks for AI applications.
• Establish metrics for model performance, latency, cost, reliability, drift, and user interaction quality.
• Guide implementation of monitoring tools and feedback loops for continuous improvement.

• Act as a trusted advisor for teams evaluating and developing AI use cases.
• Provide technical feasibility guidance and identify risks, trade‑offs, and dependencies early.
• Drive…