AI Engineering Lead/Architect
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Own end-to-end delivery of a $10M+ engineering portfolio across clients - on time, on budget, and to quality bar.
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Lead platform build, modernisation, and custom application programmes natively on cloud, spanning .NET Full-Stack, Java Distributed Systems, Python stack etc.
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Set and enforce engineering standards: architecture guardrails, code quality, DevSecOps, and release cadence across multi-team engagements.
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Manage programme risk proactively - escalate early, resolve decisively, and keep clients informed throughout.
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Embed AI tooling across the SDLC - from AI-assisted requirements and design through to automated testing, code generation, and incident response.
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Architect and operationalise agentic systems and workflows that reduce manual toil, accelerate delivery cycles, and improve output quality.
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Quantify the impact of AI adoption: establish baselines, track velocity and quality metrics, and present measurable efficiency gains to clients and leadership.
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Stay ahead of the AI tooling curve; evaluate and pilot emerging platforms (LLM orchestration, RAG pipelines, AI code assistants).
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Carry full P&L accountability for the portfolio - margin, revenue, forecasting, and commercial hygiene.
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Partner with practice, consulting, and client partner leaders to identify expansion opportunities within existing accounts and shape new pursuit strategies.
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Translate delivery track record into growth narrative - contribute to proposals, solution designs, and client presentations that differentiate on execution credibility.
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Serve as the senior delivery point-of-contact for clients - build trust-based relationships at CTO/CIO/VP level.
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Facilitate governance forums (steering committees, QBRs, escalation calls) with clarity and confidence.
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Align internal stakeholders - practice heads, resource managers, people leaders - to programme needs without bureaucratic drag.
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Lead, mentor, and grow a high-performing engineering organisation; foster a culture of ownership and continuous improvement.
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Champion individual upskilling - create structured learning pathways around AI, cloud, and modern engineering practices.
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Spot and develop next-generation delivery leaders from within the team.
B.E./B.Tech/M.E./M.Tech - Computer Science, Electronics & Telecom
Domain focus : Banking, Financial Services, Insurance, Retail & Consumer services
What You Bring
Experience & Background
10-15 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 |
