We are looking for a battle-tested AI Engineering Leader who sits at the intersection of delivery excellence and intelligent automation. This is not a role for generalists it demands someone who has shipped large-scale technology programmes across industry verticals, built or modernised cloud-native platforms, and is now leaning into AI to drive measurably faster, smarter delivery. You will anchor a delivery-led growth motion, working hand-in-glove with practice, consulting, and client partner leaders to expand the portfolio while keeping quality non-negotiable.

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, 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.

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 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).

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 up-to-date engineering practices.

Spot and develop next-generation delivery leaders from within the team.

What You Bring

Experience & Background

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 modernisation, and greenfield application programmes on cloud — not just oversight, but technical depth you can draw on in client conversations.

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.

What Success Looks Like

Outcome

How We Measure It

Delivery-led growth

Year-on-year portfolio revenue growth; new SOWs sourced from existing accounts

Execution excellence

On-time, on-budget delivery rate; CSAT scores; reduction in critical defect leakage

AI-driven efficiency

Measurable reduction in manual effort and cycle times through AI tooling; documented ROI presented to clients

People & capability

Team retention, upskilling completion rates, and promotion pipeline health

If you have led large engineering programmes, genuinely understand AI beyond the buzzwords, and thrive at the intersection of delivery and growth — we want to hear from you.

📌 Engineering leader (Agentic AI +.Net +JAVA) (New Delhi)
🏢 Ekloud Data Labs
📍 New Delhi