AI Engineering Leader

Role Description

Zensar is looking for an AI Engineering Leader. This position is open for Full Time with excellent benefits and professional growth opportunities.

What You Will Do

• Delivery & Programme Leadership
  • Own end-to-end delivery of a $10M+ engineering portfolio across clients — on time, on budget, and to quality bar.
  • Lead platform build, modernization, 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.
• AI-Driven Engineering Acceleration
  • Embed AI tooling across the SDLC — from AI-assisted requirements and design through to automated testing, code generation, and incident response.
  • Architect and operationalize 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).
• Portfolio & Revenue Growth
  • 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.
• Client & Stakeholder Engagement
  • 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.
• People & Capability Development
  • 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.

Qualifications

• 15–17 years in software engineering with a significant portion in leadership roles managing multi-team, multi-million-dollar programmes.
• Hands-on track record of delivering platform build, legacy modernization, and greenfield application programmes on cloud — not just oversight, but technical depth you can draw on in client conversations.

Requirements

• Technical Stack & Architecture
  • .NET Full-Stack (C#, ASP.NET Core, Azure-native services) and/or Java Distributed Systems (Spring Boot, microservices, Kafka, Kubernetes) — you can assess architecture quality, not just read status reports.
  • Python stack experience (FastAPI, Django/Flask, pandas, NumPy) particularly for data pipelines, AI/ML integrations, and automation scripts.
  • Cloud-native delivery on Azure, AWS, or GCP; Infrastructure as Code, CI/CD pipelines, container orchestration, and observability are second nature.
  • Practical experience designing and deploying agentic AI systems — LLM orchestration, tool-use patterns, retrieval-augmented generation, and multi-agent workflows in an enterprise context.
• AI & Automation Fluency
  • Hands-on experience with enterprise AI coding and productivity tools — GitHub Copilot / Claude (Anthropic), and / or Cursor — applied meaningfully across design, development, review, and documentation phases of the SDLC.
  • Understands where AI drives automation, acceleration, and efficiency within IT application landscapes — and equally where it introduces risk that must be managed, especially in regulated domains.
  • Ability to differentiate between AI hype and production-ready tooling; pragmatic evaluator of what to adopt, when, and how.
• Leadership & Commercial Acumen
  • Proven P&L ownership at $10M+ scale — comfortable with revenue forecasting, margin management, SOW negotiations, and change order governance.
  • Excellent stakeholder management with both internal leaders and senior client executives; able to hold a room, manage difficult conversations, and build long-term advisory relationships.
  • Growth mindset — actively invests in own learning and models the same for the team.

Benefits

• Flexible work arrangements.
• Competitive benefits package, including medical, dental, vision, and 401(k).