• 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 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.

What You Bring

Experience & Background

• 1517 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# 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

B.E./ Computer Science Electronics & Telecom

Domain focus : Banking Financial Services Insurance Retail & Consumer services