Description

Define and operationalize end-to-end AI engineering and delivery lifecycle Build and lead AI delivery organization across engineering and operations Establish and track engineering and DevSecOps KPIs Implement AI SDLC / ADLC processes and standards Select and standardize AI tools, platforms, and frameworks Align engineering practices with enterprise architecture standards Ensure compliance with AI governance, security, and risk requirements Establish and scale agile / pod-based delivery teams Drive DevSecOps, AI Ops, and LLM Ops adoption Enable platform-driven engineering with reusable components Identify and resolve delivery bottlenecks Drive fast, iterative, outcome-focused delivery execution Engage with executive stakeholders and cross-functional teams Generic Managerial Skills, If any · How have you structured teams (pods/squads) for AI delivery? · What KPIs do you track to improve engineering productivity and delivery quality? Key Words to search in Resume Pre-Screening Questionnaire Describe an end-to-end AI solution you delivered from concept to production at enterprise scale. How do you implement CI/CD and DevSecOps practices for AI/ML or LLM systems? What is your approach to AI Ops / LLM Ops (monitoring, evaluation, drift management, guardrails)? Have you built an AI platform or ecosystem from scratch? Explain your architecture and approach. How do you define and enforce AI SDLC / ADLC processes in an enterprise environment? How have you structured and led engineering teams (pods/squads) to deliver AI solutions at scale? *What are Regulated Positions "Regulated Positions” are those positions which requires TAG to recruit candidates with specific work authorizations viz., US Citizens (or) US Persons only as these may be regulated by any of the below listed

Key Skills

Education

Any Gradute