AI Architect

About the role

Zensar Technologies is hiring an AI Architect in Bangalore, Karnataka, India to shape and evolve the ZenseAI.QI and ZenseAI.AssureAI platform suite. The role involves architecting assurance lifecycles for Classical ML, Generative AI, and Agentic AI, leading evaluation-driven development, owning presales and client proposals, and presenting value to executive stakeholders. The position requires approximately 17–21 years of overall experience with substantial recent depth in AI/ML systems, LLMs, and agentic architectures.

What you’ll do

• Shape ZenseAI.QI and ZenseAI.AssureAI and architect and evolve the platform suite across both engines
• Design and refine archetype-specific assurance lifecycles for Classical ML, Generative AI, and Agentic AI, including the eight-axis agentic trajectory scorecard
• Keep the platforms LLM-agnostic and deployable on any client stack — on-prem, cloud, or hybrid
• Own technical roadmap decisions for accelerators such as the Agentic Foundry and the curated, swap-ready tooling ecosystem
• Build and govern eval suites that gate every release, and own trajectory grading, red/purple/blue-team probes, safety attestations, drift monitoring, and fairness audits
• Translate evaluation results into release decisions including eval-threshold gates, red-team severity floors, canary and shadow deployments, and rollback rehearsals
• Respond to RFPs, RFIs, and client proposals, and architect engagements across AI QA Assessment, AI QA Transformation, and Managed AI QA
• Present the ZenseAI.QI and ZenseAI.AssureAI value proposition to client stakeholders and lead client workshops and technical walkthroughs

What you’ll bring

• Approximately 17–21 years of overall experience
• Substantial recent depth in AI/ML systems, LLMs, and agentic architectures
• Ability to architect and evolve the ZenseAI.QI and ZenseAI.AssureAI platform suite
• Experience designing archetype-specific assurance lifecycles for Classical ML, Generative AI, and Agentic AI
• Ability to keep platforms LLM-agnostic and deployable on any client stack (on-prem, cloud, or hybrid)
• Experience building and governing evaluation suites (ground-truth Q&A sets, LLM-as-judge rubrics, frozen baselines)
• Experience responding to RFPs, RFIs, and client proposals and translating requirements into solution architecture
• Ability to present value propositions directly to client stakeholders including CTOs, Chief Risk Officers, and Chief AI Officers

Skills