Agentic AI Lead

Job Title: Agentic AI Lead Location: London,UK Job Type: Full Time Exp: 6+ Yrs We at Coforge are hiring for Agentic AI Lead with the following skillset: Role Purpose Lead the design and delivery of multi-agent systems and reusable AI components, ensuring that agentic solutions are scalable, secure, testable, and aligned with business outcomes. Key Responsibilities

• Own technical design and delivery of multi-agent workflows and reusable agent components.
• Guide engineers on orchestration, RAG, memory, tool use, routing, retries, and human-in-the-loop patterns.
• Define coding, testing, observability, security, and deployment standards for agentic solutions.
• Review architectures, prompts, evaluation results, and production-readiness evidence.
• Coordinate with product, domain, platform, evaluation, UX, and governance teams.
• Mentor engineers, resolve technical blockers, and improve delivery predictability.
• Monitor quality, latency, cost, reliability, and adoption after release. Required Skills
• Advanced Python and/or TypeScript engineering
• Multi-agent orchestration frameworks and design patterns
• RAG, vector search, memory, tool calling, and structured outputs
• Cloud AI platforms, APIs, containers, CI/CD, and observability
• AI evaluation, guardrails, security, and responsible-AI practices
• Technical leadership and solution review Qualifications
• Bachelor's degree in Computer Science, Engineering, IT, or equivalent experience.
• 6–9 years in software, data, automation, or AI engineering.
• Hands-on delivery of production generative-AI or agentic-AI solutions.
• Experience leading engineers or owning a technical workstream. Behavioural Competencies
• Strong technical judgement
• Coaching and mentoring ability
• Structured problem solving
• Stakeholder communication
• Ownership of quality and delivery
• Risk awareness and escalation discipline Performance Measures
• Delivery predictability and business outcomes
• Reuse of common AI components
• Agent quality, reliability, latency, and cost
• Production defect and incident rates
• Engineering standards and review quality
• Team capability and productivity