AI Architect
At a glance
Mid-level AI Engineering role at Fa Etvl Saasfaprod1. Pune.
Pay not stated AI Architect salary
aimlroles summary, based on the employer's posting.
What you'll do
- Set architecture and roadmap for both platform engines, including AI assurance lifecycles and archetype-specific tooling
- Build release-gating evaluation suites and use results to set thresholds, deployment steps and rollback decisions
- Shape client proposals, estimates and solution designs across assessment, transformation and managed-service engagements
- Present technical value to client executives, lead workshops and demonstrate solutions in sales pursuits
- Develop reusable practice assets and coach specialists in emerging AI assurance roles
What you bring
- Around seventeen to twenty-one years in technology, with substantial hands-on quality engineering and testing experience
- You understand agentic system design, including orchestration, tool use, memory and risks such as drift or leakage
- You have built or governed evaluation methods for generative or agentic AI, including baselines, judge rubrics and adversarial tests
- You can own technical proposal content and present confidently to senior client stakeholders
- You know the EU AI Act, NIST AI RMF and ISO/IEC 42001
Who this fits
This mid-seniority role suits an experienced quality engineering architect who can bridge conventional testing and AI assurance. It also calls for direct client-facing work, including executive presentations, workshops and sales demonstrations. The role is based in Pune, India.
From the employer
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 — 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 practice's proof-first approach to closing.
Represent the practice at partner and industry forums as the practice's 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 practice's 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.
Must-Have
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.
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.