AI Implementation Engineer

We are seeking a Staff IT AI Implementation Engineer to lead the architecture, deployment, and operational oversight of our enterprise AI ecosystem. Sitting at the intersection of hands-on technical execution and strategic governance, this role is pivotal in building production-ready AI solutions, automating cross-functional workflows, and shaping responsible AI adoption standards across the organization.

This position is for local candidates only (CA - Milpitas / San Jose area) who can report in person. Remote or out-of-area candidates will not be considered.

• AI Platform Administration: Manage enterprise AI platforms (including frontier LLMs like Claude Enterprise and ChatGPT Enterprise), ensuring secure access controls and seamless coordination with IT infrastructure.
• Workflow Automation & Agent Development: Design, build, deploy, and maintain custom AI agents and automated workflows to solve business challenges while adhering to IT security standards.
• Cross-Functional Advisory: Serve as the primary point of contact for AI requests across all business units. Partner with stakeholders to assess feasibility, scope technical requirements, and deliver production-ready implementations.
• Governance & Security Compliance: Collaborate with IT Security and leadership to establish, implement, and enforce AI governance frameworks and acceptable use policies.
• Vendor & Tool Evaluation: Review and vet emerging AI tools, services, and platform releases alongside IT Security to provide recommendations for adoption or integration.
• Enablement & Training: Develop cross-departmental guidance, best practices, and training programs to support safe, compliant, and effective AI usage.
• Performance & Cost Optimization: Track platform usage metrics, monitor cost allocations, and present operational insights and optimization strategies to leadership.
• Security Reviews: Ensure all agents, MCP servers, integrations, and data connections undergo rigorous IT Security review before deployment, upholding principles of least-privilege access.

• Experience: 3+ years of experience in software engineering, data science, ML engineering, or applied AI (or 2+ years specialized in building production LLM applications).
• AI Model Integration: 1–3 years of hands-on experience working with frontier AI services, LLMs, or agent development.
• Agent Architecture: Proven ability to build AI agents utilizing tool/function calling, API integrations, and Model Context Protocol (MCP) frameworks.
• RAG Pipelines: Technical proficiency in Retrieval-Augmented Generation (RAG) architecture, including embeddings, vector databases, chunking strategies, and retrieval evaluation.
• LLM Evaluation: Hands-on experience evaluating LLM performance using test sets, LLM-as-judge frameworks, and prompt regression testing.
• Prompt & Context Engineering: Advanced expertise in production-grade prompt engineering and context optimization.
• AI Security Standards: Strong knowledge of AI-specific security threats (e.g., prompt injection, data leakage, PII handling) and familiarity with the OWASP Top 10 for LLMs.
• Deployment & Managed Services: Experience containerizing AI applications, managing self-hosted/served models, and utilizing cloud AI services (e.g., AWS Bedrock, Azure OpenAI/AI Foundry, Google Vertex AI).
• Governance Frameworks: Familiarity with responsible‑AI practices and governance models (such as NIST AI RMF or ISO/IEC 42001).
• Communication &
  
  Collaboration:
  
  Demonstrated track record of collaborating across business units and translating complex technical concepts for non-technical stakeholders.

• Certifications:
  • Anthropic Claude Certified Associate / Developer
  • AWS Certified AI Practitioner or AWS…