01Key Responsibilities
AI Platform Architecture & Capabilities Design and evolve a shared enterprise AI platform that supports multiple projects, business units, and product teams. Define reusable platform capabilities for GenAI, RAG, model access, orchestration, prompt management, evaluation, observability, governance, and security. Build and maintain AI/model gateways, service catalogs, reusable APIs, SDKs, platform integrations, and enterprise self-service tools . Establish platform standards and reference patterns that enable teams to consume approved AI capabilities consistently and securely. Evaluate emerging technologies and mature proven approaches into scalable enterprise platform capabilities. MLOps, LLMOps & CI/CD Build and maintain CI/CD pipelines for AI services, models, prompts, agents, configurations, and platform components. Implement MLOps/LLMOps practices for model lifecycle management, versioning, evaluation, deployment, rollback, monitoring, and release governance. Develop automated evaluation frameworks for quality, grounding, safety, latency, reliability, and model performance. Implement enterprise observability across model calls, prompts, integrations, token usage, cost, logs, traces, and platform health. Standardize production-readiness, deployment, and promotion patterns across projects, environments, regions, and cloud platforms. Multi-Cloud Integration, Enterprise Tools & Support Design and operate AI platform capabilities across Azure, AWS, and Snowflake , with support for multi-cloud and multi-region deployment patterns. Build reusable enterprise integrations connecting AI services with internal applications, data platforms, APIs, identity services, and approved third-party platforms. Develop and support shared enterprise tools, APIs, automation, and platform services that can be reused across multiple projects. Provide hands-on production support , including incident triage, root-cause analysis, performance tuning, troubleshooting, and operational improvements. Design for scalability, reliability, resilience, regional availability, security, cost efficiency, and maintainability across platform services and integrations. Security, Governance & Platform Standards Embed security-by-design, privacy-by-design, Responsible AI, and compliance-by-design into platform capabilities. Implement IAM, audit logging, access controls, traceability, data protection, AI security guardrails, and policy enforcement. Define reusable reference architectures, APIs, SDKs, design patterns, ADRs, governance controls, and platform standards. Partner with Cybersecurity, Privacy, Quality, Enterprise Architecture, and engineering teams to translate governance requirements into practical technical controls. Technical Leadership Act as a senior technical contributor , helping project teams adopt and integrate shared AI platform capabilities. Provide technical guidance through design reviews, code reviews, reference implementations, and mentoring. Translate project and business requirements into reusable, scalable platform capabilities while remaining engaged in implementation and support. Required Qualificaitons: Bachelors or Masters degree in Computer Science, Engineering, Data Science, or a related technical field. Strong experience designing and operating enterprise platforms used by multiple projects or product teams. Strong hands-on Python and API integration skills, with experience building reusable platform services and enterprise tools. Proven experience with GenAI / LLM platform capabilities , including RAG, model integration, evaluation, observability, and lifecycle management. Working knowledge of Model Context Protocol (MCP) , including MCP-based integration patterns, tool connectivity, security, and .
