• End-to-end AI lifecycle delivery
• DevSecOps + AI Ops + LLM Ops expertise
• AI platform & internal developer platform (IDP) experience
• AI SDLC / ADLC process design & governance Greenfield enterprise AI ecosystem build
• Strong delivery leadership & executive stakeholder management
Roles & Responsibilities
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
per MSA.
- ITAR (International Traffic in Arms Regulations).
- NERC CIP (NERC Critical Infrastructure Protection).
- NRC (Nuclear Regulatory Commission).
- Any other regulations as appropriate.
Role Descriptions: Quality Engineering (QE) Leader
Essential Skills: Quality Engineering (QE) Leader
Desirable Skills:
Keyword:
Skills: Generative AI - Quality Assurance
Experience Required: 8-10, Project Code :
• DevSecOps + AI Ops + LLM Ops expertise
• AI platform & internal developer platform (IDP) experience
• AI SDLC / ADLC process design & governance Greenfield enterprise AI ecosystem build
• Strong delivery leadership & executive stakeholder management
Roles & Responsibilities
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
per MSA.
- ITAR (International Traffic in Arms Regulations).
- NERC CIP (NERC Critical Infrastructure Protection).
- NRC (Nuclear Regulatory Commission).
- Any other regulations as appropriate.
Role Descriptions: Quality Engineering (QE) Leader
Essential Skills: Quality Engineering (QE) Leader
Desirable Skills:
Keyword:
Skills: Generative AI - Quality Assurance
Experience Required: 8-10, Project Code :