[Hiring] AI Architect @Zensar

Role Description

Shape ZenseAI.QI and ZenseAI.AssureAI.

• Architect and evolve the platform suite across both engines: the 18-agent ZenseAI.QI stack (DeepSpeci, CaseGeni, Auto-PlayPilot, DataGeni, Secure-Xi, Perf-Xi, Insights360, RIA and Accessibility Intelligence) and the four-pillar ZenseAI.AssureAI framework (Data Quality, Model Evaluation, Trustworthiness, and Scalability & Security).
• Design and refine the archetype-specific assurance lifecycles for Classical ML, Generative AI and Agentic AI, including the signature eight-axis agentic trajectory scorecard graded on every run.
• 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 (a 25-blueprint reference matrix) and the curated, swap-ready tooling ecosystem around each archetype.
• Lead Evaluation-Driven Development.
• Build and govern eval suites — ground-truth Q&A sets, LLM-as-judge rubrics, frozen baselines.
• Own trajectory grading, red/purple/blue-team probes and safety attestations for agentic and generative systems.
• Translate evaluation results into release decisions: eval-threshold gates, red-team severity floors, canary and shadow deployments, and rollback rehearsals.
• Bring evaluation-driven development practice into client engagements.
• Own Presales and Client Proposals.
• Respond to RFPs, RFIs and client proposals across both engines.
• Architect engagements across the full ladder — AI QA Assessment, AI QA Transformation, Managed AI QA.
• Build estimates, staffing plans and technical win themes.
• Present and Articulate Value to Clients.
• Present the ZenseAI.QI and ZenseAI.AssureAI value proposition directly to client stakeholders.
• Lead client workshops and technical walkthroughs.
• Build and deliver executive trust scorecards and portfolio risk heat maps.
• Represent Zensar in Client Captives for Sales.
• Present live in client captive sessions and sales pursuits.
• Run and narrate proof-of-concept demonstrations.
• Represent the practice at partner and industry forums.
• Build Practice IP and Mentor the Next Generation.
• Contribute reusable accelerators, reference architectures and industry packs.
• Mentor and help build out the practice's emerging AI-specialist roles.
• Support GenAI and AI-enablement training for QI associates.

Qualifications

• Approximately 17–21 years of overall technology experience, including a substantial, hands-on foundation in Quality Engineering / Test Engineering.
• Working architectural knowledge of agentic AI systems: multi-agent orchestration, tool use, memory, and operational risks specific to agents.
• Direct experience with evaluation-driven development: building or governing eval suites, LLM-as-judge frameworks, ground-truth datasets, frozen baselines and red-teaming / adversarial testing.
• Strong client-interfacing capability: comfortable owning technical content in an RFP response and presenting to CXO-level stakeholders.
• Ability to talk convincingly across both deterministic QE and probabilistic AI assurance.
• Familiarity with the regulatory and standards backdrop shaping AI assurance conversations: EU AI Act, NIST AI RMF and ISO/IEC 42001.

Requirements

• Approximately 17–21 years of overall technology experience, including a substantial, hands-on foundation in Quality Engineering / Test Engineering.
• Working architectural knowledge of agentic AI systems.
• Direct experience with evaluation-driven development.
• Strong client-interfacing capability.
• Ability to talk convincingly across both deterministic QE and probabilistic AI assurance.
• Familiarity with the regulatory and standards backdrop shaping AI assurance conversations.

Benefits

• Experience-led everything.
• Commitment to conceptualizing, designing, engineering, marketing, and managing digital solutions.
• Inclusive workplace with equal employment opportunity.