AI Architect

HCLTech; Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

HCLTech in London seeks an Agentic Forward Deployed Engineer to embed with client teams and turn ambiguous business problems into production agents. You own agents end to end—from concept to deployment and sustenance—driving measurable ROI with Python and responsible AI.

You will lead a small team of engineers, design scalable agent architectures, and ensure secure integration with client ecosystems while maintaining high standards of safety and reliability.

Qualifications

• Experience delivering enterprise AI solutions.
• Strong Python development skills.
• Familiarity with agent-based workflows.

Responsibilities

• Conceptualize and prototype agentic solutions with stakeholders.
• Design and ship single-agent and multi-agent systems in Python.
• Lead a small team to deliver client work.
• Ensure agent integration via standards-based APIs and secure auth.
• Build reusable components and promote safe, predictable behavior.
• Run eval-driven development and CI/CD for agents.

Skills

Python

Agentic AI

Prompt engineering

CI/CD

DevSecOps

Team leadership

Tools

ADKs

Job description

HCLTech is a global technology company, home to 219,000+ people across 54 countries, delivering industry-leading capabilities centered on digital, engineering and cloud, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of $13+ billion.

For more information on how we process your personal data, please refer to HCLTech’s Candidate Data Privacy Notice.

Job Summary

As an Agentic Forward Deployed Engineer, you operate at the front line of delivery - embedded with the client, turning ambiguous business problems into production agents, fast. Your deliverable is Business Transformation Agents: autonomous and multi-agent systems that automate and reimagine real business processes such as invoice disputes, procurement approvals, onboarding, claims and compliance workflows. You own each agent end to end -conceptualize, build, integrate, evaluate, deploy, and sustain - and you lead a small team to do the same. You build exclusively in Python using agent development kits, and you bring Agentic AI capabilities to life inside the client's world, with Responsible AI, evaluation and security as non-negotiables

Key Responsibilities

• Conceptualize fast: embed with stakeholders, frame a business process as an agentic solution, and stand up a working agent prototype in days, not weeks.
• Build Business Transformation Agents: design and ship single-agent and multi-agent systems in Python using ADKs that automate and transform real client workflows, with measurable ROI.
• Own efficiency as the scorecard: drive delivery efficiency and operational efficiency; shorter cycle times, less manual effort, higher accuracy, lower cost-to-serve.
• Engineer the agent core: apply prompt engineering, context engineering, prompt caching, RAG / context-graph retrieval, memory, tool / function calling, MCP integration and multi-agent orchestration.
• Integrate to standards: connect agents into client ecosystems through proven integration patterns, standards-based APIs and secure authentication.
• Make reusability and predictability the default: build reusable agent components, skills, tool libraries and templates; add guardrails so agent behaviour is predictable, safe and repeatable.
• Prototype and iterate quickly: use the kit's scaffolding to prototype, then harden to production-grade, well-tested Python.
• Run eval-driven development: build evaluation harnesses and test suites that measure agent correctness, safety and regression before anything ships.
• Own AgentOps / DevSecOps: CI/CD for agents, versioning, observability and telemetry, shift-left security, and Responsible AI governance baked in from day one.
• Run a continuous, adaptable feedback loop: feed production telemetry, evals and client feedback back into prompts, context and agent design.
• Stay ahead of the curve: adopt evolving agent frameworks and patterns quickly and bring field learnings back to the practice.
• Lead and mentor: set technical direction for a lean team of 3 agent engineers, raise the engineering bar, and grow the pod's agentic capability.