Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Lead AI delivery programs and design multi-agent systems for global clients.
- Company: Join Zensar, a leader in digital solutions and technology services.
- Benefits: Competitive salary, recognition for your impact, and a supportive team culture.
- Why this job: Make a real difference in AI while working with cutting-edge technologies.
- Qualifications: Degree-level education and proven experience in deploying agent-based systems.
- Other info: Mentor junior engineers and contribute to innovative AI practices.
The predicted salary is between 80000 - 100000 £ per year.
Zensar is a leading digital solutions and technology services company that specialises in partnering with global organisations across industries in their Digital Transformation journey. Zensar's Return on Digital strategy has enabled customers to look beyond current investments towards realising visible business benefits in their digital transformation journey.
If you're looking for a workplace where associates realise and contribute to their full potential, are recognised for the impact they make, and enjoy the company of the people they work with, then you've come to the right place!
Role description:
This is not a slide-making or prompt-engineering role. We are looking for someone who has built multi-agent AI systems that run in production - not demos, not pilots that died after a sprint. You will anchor AI delivery programs end-to-end, work directly with global clients, and stay sharp on a field that changes every few weeks. You will report into and replicate the function of a senior AI delivery leader - which means you need both the depth to architect solutions and the presence to walk a CXO through what you built and why it works.
Duties and Responsibilities
- Delivery & Architecture
- Own end-to-end delivery of AI-native programs - from architecture through production deployment
- Design and build multi-agent orchestration systems using LangChain, LangGraph, CrewAI, or equivalent
- Integrate agent systems with enterprise surfaces: APIs, ERPs, CRMs, data platforms - not toy datasets
- Define agent topology: tool routing, memory strategy, state machines, fallback handling
- Agentic Coding & Development
- Run agentic coding workflows using Claude Code, Cursor, OpenAI Codex, or equivalent CLI tools
- Lead projects where AI writes significant portions of the codebase - and you guide, review, and ship it
- Work with CLAUDE.md, shared context frameworks, and multi-session agent setups for team use
- Debug non-deterministic agent outputs systematically - not by gut feel
- Client & Stakeholder Engagement
- Translate business problems into agent architectures for global CXO-level stakeholders
- Run discovery workshops, solution reviews, and delivery cadences with client teams
- Prepare and present technical proposals, POC plans, and roadmaps - own the story end-to-end
- Team & Practice
- Mentor junior AI engineers; raise AI engineering quality across the delivery team
- Stay current: evaluate new models, frameworks, and tooling before the hype catches up
- Contribute to internal knowledge bases, reusable frameworks, and accelerators
Technical Skills Required
- Proven experience of:
- Agent Orchestration: LangChain, LangGraph, CrewAI - not just conceptual
- Agentic Coding Tools: Claude Code CLI, Cursor, OpenAI Codex, Copilot
- RAG & Vector Stores: Chroma, Weaviate, Pinecone, know where RAG breaks
- LLM APIs & SDKs: Anthropic, OpenAI, Gemini - prompt design, tool use
- Python / TypeScript: Primary languages for agent + backend development
- LangSmith / Observability: Tracing, evaluation, debugging agent runs
- Cloud Platforms: Azure, AWS, GCP (at least one) - deployment, infra, managed services
- API & System Integration: REST, gRPC, Kafka - enterprise integration patterns
- MCP / Shared Context: Model Context Protocol, CLAUDE.md, Beads
- Agent Evaluation: Testing non-deterministic outputs, guardrails, evals
- CI/CD & DevOps: Git, containers, pipelines - agents need to ship
- Client Communication: Can present architecture to a CXO without jargon
Must have:
- Deployed 23 agent-based systems in production - stateful, multi-step, real users
- Used LangGraph for multi-agent orchestration with memory, tool routing, and state management
- Built projects where AI (Claude Code, Codex, Cursor) wrote significant portions of the code
- Implemented RAG pipelines end-to-end - chunking, embedding, retrieval, re-ranking, evaluation
- Integrated agents with real enterprise APIs - not just OpenAI playground or sample data
- Debugged a production agent failure - and fixed it without blaming the model
- Can articulate when NOT to use agents - that is how we know you have built things
Bonus - Real Differentiators
- Experience with Claude Code CLI in team environments (CLAUDE.md, shared context, multi-session flows)
- Familiarity with LangSmith for agent tracing, evaluation pipelines, and debugging at scale
- Has shipped something using MCP (Model Context Protocol) or similar shared-context tooling
- QA/testing mindset for agents - systematic evaluation of non-deterministic outputs
- Background in IT services or consulting - managing client expectations while building
- Experience with SLMs, fine-tuning, or on-device/edge agent deployment
Qualification:
Must be educated to at least degree level or equivalent.
AI Lead employer: Zensar Technologies
Contact Detail:
Zensar Technologies Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Lead
✨Tip Number 1
Network like a pro! Attend industry meetups, webinars, and conferences where you can connect with other AI enthusiasts and professionals. Don't be shy to introduce yourself and share your passion for AI – you never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving multi-agent systems and real-world applications. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with CXOs and clients. Mock interviews with friends can help you nail your delivery!
✨Tip Number 4
Apply through our website! We love seeing candidates who are genuinely interested in joining us at StudySmarter. Tailor your application to highlight your experience with agent orchestration and coding tools, and let your enthusiasm shine through!
We think you need these skills to ace AI Lead
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the AI Lead role. Highlight your experience with multi-agent systems and any relevant projects you've led. We want to see how your skills align with what we're looking for!
Showcase Your Technical Skills: Don’t hold back on detailing your technical expertise! Mention specific tools like LangChain, Claude Code, and any cloud platforms you’ve worked with. We love seeing candidates who can demonstrate their hands-on experience.
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language to explain your past experiences and how they relate to the role. We appreciate a well-structured application that gets straight to the point!
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at Zensar Technologies
✨Know Your Tech Inside Out
Make sure you’re well-versed in the specific technologies mentioned in the job description, like LangChain and Claude Code. Prepare to discuss your hands-on experience with these tools and how you've used them in real-world applications.
✨Showcase Your Problem-Solving Skills
Be ready to share examples of how you've tackled complex AI challenges in the past. Think about specific projects where you’ve designed multi-agent systems or debugged production failures, and explain your thought process clearly.
✨Engage with Stakeholders
Since this role involves client interaction, practice explaining technical concepts in a way that’s accessible to non-technical stakeholders. Prepare to discuss how you would translate business problems into effective AI solutions for CXOs.
✨Demonstrate Leadership and Mentorship
Highlight any experience you have mentoring junior engineers or leading projects. Zensar values collaboration, so be prepared to discuss how you’ve raised the quality of work within your team and contributed to knowledge sharing.