Associate Director - AI Infrastructure & Platform Engineering

Job Description Summary

Lead architecture and engineering deliveries and modernization of Enterprise AI Infrastructure & Platform Engineering domain, responsible for ensuring high-quality solution design, integration and governance that meet organization requirements, align with enterprise standards, and enable reliable delivery of complex initiatives.

The role requires strong technology leadership, enterprise architecture and engineering solution expertise, and the ability to build trusted partnerships across Enterprise Architecture, Hybrid Infrastructure, Security, Identity & Access Management, Networking, End User Computing, Operations, AI Enablement, and Business Technology teams

Job Description

Key Responsibilities:

• Execute the Enterprise AI Infrastructure & Platform Engineering strategy aligned with enterprise technology roadmap and business objectives. Translate architecture direction and roadmaps into detailed engineering designs, delivery plans, and reusable platform components.

• Evaluate and drive adoption of emerging AI technologies, ensuring the organization is positioned to leverage innovation securely and efficiently. Establish repeatable engineering patterns, reference implementations, and reusable platform components to accelerate enterprise AI adoption

• Drive engineering of AI developer environments, AI workspaces, AI APIs, model access patterns, AI agent frameworks, RAG solutions, vector databases, and MCP-based integrations. Deliver scalable, resilient, and secure AI platform services supporting enterprise AI initiatives.

• Ensure platform reliability, observability, resilience, disaster recovery, and operational readiness. Ensure alignment with Enterprise Architecture, Infrastructure Security & Compliance, Risk, and Infrastructure standards.

• Oversee onboarding and integration of enterprise AI platforms (e.g., ChatGPT Enterprise, Claude, Gemini, Azure AI Foundry, AWS Bedrock, AgentCore, Vertex AI, Run.AI, SLURM, and open-source AI platforms) with enterprise identity, networking, security, monitoring, and operational services.
• Ensure engineering deliverables align with Enterprise Architecture, Infrastructure Security & Compliance, Risk, and Infrastructure standards
• Ensure technical documentation, implementation guides, operational runbooks, and engineering knowledge assets are maintained to high standards
• Provide hands-on technical leadership across the AI Infrastructure & Platform Engineering team, guiding solution design, technology choices, and engineering standards.
• Review and approve technical designs, engineering patterns, and platform implementations delivered by engineers.
• Collaborate with business stakeholders and enterprise technology teams to deliver enterprise AI capabilities. Foster a culture of engineering excellence, innovation, automation, continuous learning, and customer-centricity.

Essential Requirement:

• Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related discipline . Master’s degree preferred
• 12+ years of experience in Enterprise Hybrid Infrastructure, Platform Engineering, Enterprise Architecture, or related technology domains.
• Proven experience in designing, engineering, and operating enterprise-scale infrastructure and platform services across large organizations.
• Demonstrated experience in engineering enterprise AI infrastructure and platform capabilities supporting Generative AI, AI/ML workloads, and AI-enabled applications.
• Experience designing and operationalizing secure AI development environments, enterprise AI platform services, AI APIs, identity integration, networking, platform security, observability, and operational support.
• Exposure to enterprise AI ecosystems such as ChatGPT Enterprise, Claude, Gemini, Azure AI Foundry, AWS Bedrock, AgentCore, Vertex AI, Strands Agents SDK, LangGraph, CrewAI, Run.AI or SLURM or open-source AI platformsis highly desirable.
• Experience with AI platform architecture, GPU/HPC infrastructure, AI compute environments, AI gateways, vector databases, model access patterns, and enterprise AI governance is an advantage.
• Enterprise AI Platforms and AI developer environments (JupyterLab, VS Code, AI Workstations, Claude Cowork).

Desirable Requirement:

• Kubernetes & Container Platforms. Infrastructure as Code (Terraform, Bicep, ARM, Ansible) and CI/CD platforms (GitHub Actions, Bitbucket, Azure DevOps, Jenkins).
• AI Model Integration & A…