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# Agentic AI Architect

- **Company:** HCLTech
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Continuous Integration, Python (Programming Language), Management of Software Versions, Workflow Management Systems, Large Language Models, Multi-Agent Systems, Prompt Engineering, Caching, Virtual Agents, Devsecops
- **Published:** July 10, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2867531255-2

## About the Role

* python
 * LangGraph
 * Open AI Agents SDK
 * Model Context Protocol (MCP)
 * Amazon Bedrock
 * Large Language Models (LLMs)

## Description

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, 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.


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