Full-Time
AI Engineering Lead/Architect
Deadline 8/16/26
Zensar
No salary listed
Pune, Maharashtra, India
In Person
Category
 Engineering Management (1) |
|---|
Required Skills
Python
Java
.NET
DevOps
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Requirements
- Experience delivering end-to-end engineering portfolios of at least $10M across multiple clients, ensuring on-time, on-budget delivery and adherence to quality standards.
- Experience leading platform build, modernization, and custom application programmes natively on cloud, spanning .NET Full-Stack, Java Distributed Systems, and Python stack.
- Ability to set and enforce engineering standards including architecture guardrails, code quality, DevSecOps, and release cadence across multi-team engagements.
- Proven ability to manage programme risk proactively, escalate early, resolve decisively, and keep clients informed.
- Experience carrying full P&L accountability for a portfolio including margin, revenue, forecasting, and commercial hygiene.
- Experience partnering with practice, consulting, and client leaders to identify expansion opportunities within existing accounts and shape new pursuit strategies.
- Ability to translate delivery track record into growth narratives for proposals, solution designs, and client presentations that differentiate on execution credibility.
- Senior client relationship management at CTO/CIO/VP level and ability to facilitate governance forums (steering committees, QBRs, escalation calls).
- Ability to align internal stakeholders to programme needs without bureaucratic drag.
- Leadership experience in mentoring and growing a high-performing engineering organisation; fostering a culture of ownership and continuous improvement.
- Championing individual upskilling in AI, cloud, and modern engineering practices.
Responsibilities
- Own end-to-end delivery of a $10M+ engineering portfolio across clients — on time, on budget, and to quality bar.
- Lead platform build, modernisation, and custom application programmes natively on cloud, spanning .NET Full-Stack, Java Distributed Systems, Python stack etc.
- Set and enforce engineering standards: architecture guardrails, code quality, DevSecOps, and release cadence across multi-team engagements.
- Manage programme risk proactively — escalate early, resolve decisively, and keep clients informed throughout.
- Embed AI tooling across the SDLC — from AI-assisted requirements and design through to automated testing, code generation, and incident response.
- Architect and operationalise agentic systems and workflows that reduce manual toil, accelerate delivery cycles, and improve output quality.
- Quantify the impact of AI adoption: establish baselines, track velocity and quality metrics, and present measurable efficiency gains to clients and leadership.
- Stay ahead of the AI tooling curve; evaluate and pilot emerging platforms (LLM orchestration, RAG pipelines, AI code assistants).
- Carry full P&L accountability for the portfolio — margin, revenue, forecasting, and commercial hygiene.
- Partner with practice, consulting, and client partner leaders to identify expansion opportunities within existing accounts and shape new pursuit strategies.
- Translate delivery track record into growth narrative — contribute to proposals, solution designs, and client presentations that differentiate on execution credibility.
- Serve as the senior delivery point-of-contact for clients — build trust-based relationships at CTO/CIO/VP level.
- Facilitate governance forums (steering committees, QBRs, escalation calls) with clarity and confidence.
- Align internal stakeholders — practice heads, resource managers, people leaders — to programme needs without bureaucratic drag.
- Lead, mentor, and grow a high-performing engineering organisation; foster a culture of ownership and continuous improvement.
- Champion individual upskilling — create structured learning pathways around AI, cloud, and modern engineering practices.
- Spot and develop next-generation delivery leaders from within the team.
Desired Qualifications
- Experience embedding AI tooling across the SDLC and driving measurable efficiency gains.
- Experience architecting and operationalising agentic systems and workflows that reduce manual toil and accelerate delivery cycles.
- Ability to quantify AI adoption impact with baselines, velocity and quality metrics, and present measurable efficiency gains to clients and leadership.
- Experience evaluating and piloting emerging AI platforms (LLM orchestration, RAG pipelines, AI code assistants).
- Proven ability to translate delivery success into compelling growth narratives for proposals and client presentations.