AI Architect

Job Summary

Shape ZenseAI.QI and ZenseAI.AssureAI

Responsibilities

• 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 - as model-mixing and constant provider churn become the market norm.
• 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 - that gate every release rather than validate it after the fact.
• Own trajectory grading, red/purple/blue-team probes and safety attestations for agentic and generative systems, and drift monitoring and fairness audits for classical ML.
• 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 - showing, not just telling, how a live harness beats a one-time audit.
• Own Presales and Client Proposals
• Respond to RFPs, RFIs and client proposals across both engines, translating client requirements into a defensible solution architecture and commercial structure.
• Architect engagements across the full ladder - AI QA Assessment, AI QA Transformation, Managed AI QA - and fast-starts such as the LLM Health Check, Agent Stress Test and Compliance Sprint.
• Build estimates, staffing plans and technical win themes that hold up under client and internal scrutiny, across client-managed, risk-reward and Zensar-managed commercial models.
• Present and Articulate Value to Clients
• Present the ZenseAI.QI and ZenseAI.AssureAI value proposition directly to client stakeholders - from QA Directors and Heads of AI/ML Engineering to CTOs, Chief Risk Officers and Chief AI Officers - adapting the narrative to each audience.
• Lead client workshops and technical walkthroughs, including guiding a CIO through the Agentic Foundry in a single session.
• Build and deliver executive trust scorecards and portfolio risk heat maps that make the assurance story board-ready.
• Represent Zensar in Client Captives for Sales
• Present live in client captive sessions and sales pursuits as the senior technical voice for Quality Intelligence, alongside account and sales leadership.
• Run and narrate proof-of-concept demonstrations that turn a client's stated risk into a small, credible proof - the practices proof-first approach to closing.
• Represent the practice at partner and industry forums as the practices thought leadership and pipeline develop.
• Build Practice IP and Mentor the Next Generation
• Contribute reusable accelerators, reference architectures and industry packs across BFSI, TMT, and Manufacturing & Retail back into the practices IP base.
• Mentor and help build out the practice's emerging AI-specialist roles - Prompt Engineer, LLM-Eval Engineer, Agent Architect, Trajectory Eval Engineer, Knowledge/RAG Engineer, AI Security Analyst and Adversarial Red/Blue Team Lead.
• Support GenAI and AI-enablement training for QI associates across the practice.

• Approximately 17-21 years of overall technology experience, including a substantial, hands-on foundation in Quality Engineering / Test Engineering - automation architecture, test strategy and the full software testing lifecycle - not AI experimentation layered on top of a thin QE background.
• Working architectural knowledge of agentic AI systems: multi-agent orchestration, tool use, memory, and the operational risks specific to agents - unauthorised tool calls, trajectory drift, memory leakage across accounts.
• 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 for generative or agentic systems.
• Strong client-interfacing capability: comfortable owning technical content in an RFP response, presenting to CXO-level stakeholders, and holding the room in a live client or sales captive setting.
• Ability to talk convincingly across both deterministic QE and probabilistic AI assurance - the one estate narrative is the core sell, and the architect has to embody it, not just recite it.
• Familiarity with the regulatory and standards backdrop shaping AI assurance conversations: EU AI Act, NIST AI RMF and ISO/IEC 42001.

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