Platform Engineer III
- London, United Kingdom
نُشرت أول أمس
عن الوظيفة
City Of London, England
Job Summary
About the role You earn trust by solving the customer’s problem with working agentic AI systems. The work starts close to the user and ends close to production. In this role you design, build, integrate and productionise agentic AI solutions inside customer environments. You turn unclear needs into working systems: AI agents, multi agent workflows, tool and API integrations, retrieval and data pipelines, evaluations and the cloud-native platforms that run them. You own this end to end, from discovery and design through build, integration and production rollout, working alongside the customer’s engineering and domain teams. You work across the major agentic AI stacks rather than a single vendor. Depending on the engagement, that means building with Anthropic, OpenAI, Google or Microsoft, and with vendor-neutral frameworks such as LangGraph where the customer wants to stay portable across models. You prove the idea quickly in a visual builder when that helps the customer. You then move to governed, code-first delivery, where evidence, tests, evaluation and release confidence turn the prototype into verified value. You pick the right model, framework and harness for the customer’s constraints, then make it reliable in their environment. Success is measured by what the customer can run in production and the value it delivers, not by the demo. In the FDE Practice this role covers both value engineering and quality engineering. The value engineering emphasis is implementation and user value. The quality engineering emphasis is evaluation, reliability, release safeguards and production readiness. You may lean one way or the other depending on the engagement. You will use coding agents where they increase speed, but you stay responsible for direction, review and control. You supervise agent output, inspect assumptions, write or refine code and hold the line on enterprise delivery standards.
Key Responsibilities
What you will do • Build agentic AI solutions: single and multi-agent systems, tool use, retrieval pipelines, automation and prototypes that reach production. • Build on the major agentic stacks, including the Claude Agent SDK, OpenAI Agents SDK, Google Agent Development Kit, Microsoft Agent Framework and vendor neutral frameworks such as LangGraph, choosing the right one for the customer’s needs. • Work with customer technical teams to understand systems, constraints, data, deployment paths and operating needs. • Use coding agents and human engineering judgement to move quickly while preserving review, test and release discipline. • Integrate agents with enterprise systems, APIs, cloud services, databases, ticketing systems, model services and security controls, often through MCP and similar tooling. • Implement agent evaluations, automated tests, observability hooks, runbooks and release safeguards. • Troubleshoot implementation, deployment, data, model and integration issues. • Codify what works into reusable patterns, starter kits and playbooks, alongside technical decisions, defects, risks and lessons learned, to raise the floor for the wider practice. • Feed field insight back to the platform vendors through our partnerships, turning recurring friction and gaps into product feedback that shapes the tools you build on. • Support handover and production readiness with clear documentation and operational ownership. • Work with architects and delivery managers to keep implementation aligned to value and technical guardrails.
What we are looking for These are the essentials. If you meet most of them, we want to hear from you. • Several years of production software engineering experience in Python, TypeScript, Java or comparable stacks. • Hands-on experience building with LLMs and agents on at least one major platform, such as Anthropic, OpenAI, Google or Microsoft. • Practical understanding of agentic patterns: tool use, retrieval, prompting, model behaviour, evaluation and multi-agent workflows such as orchestration, delegation and self-reflection. • Experience with APIs, databases, cloud platforms, integration surfaces or comparable deployment environments. • Strong debugging, testing and integration discipline. • Experience working in ambiguous delivery contexts with customers, users or cross functional technical teams. • Ability to balance fast prototyping with production-quality engineering.
Skill Requirements
Nice to have These would strengthen your application, but they are not deal-breakers. We do not expect every candidate to bring all of them. • Experience taking agentic or AI systems from prototype to production rollout. • Experience with agent orchestration frameworks, MCP servers or custom tool integrations. • Experience with data and retrieval pipelines, workflow automation or model deployment. • Experience with evaluation, observability, troubleshooting or release safeguards for AI systems. • A vendor certification on one of the major AI platforms (Anthropic, OpenAI, Google or Microsoft).
Other Requirements
Who does well here The strongest people in this role are: • Hands-on and pragmatic, with a bias towards working software. • Careful about quality, testing, integration and operational consequences. • Comfortable pairing with customers, architects, engineers and agents. • Quick to learn unfamiliar domains, APIs and systems. • Clear in writing, especially around decisions, assumptions, tests and handover notes. • Willing to challenge generated code and weak assumptions rather than accept output at face value. • Focused on user value without ignoring maintainability and production risk.
#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply-#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply-
Job Summary
About the role You earn trust by solving the customer’s problem with working agentic AI systems. The work starts close to the user and ends close to production. In this role you design, build, integrate and productionise agentic AI solutions inside customer environments. You turn unclear needs into working systems: AI agents, multi agent workflows, tool and API integrations, retrieval and data pipelines, evaluations and the cloud-native platforms that run them. You own this end to end, from discovery and design through build, integration and production rollout, working alongside the customer’s engineering and domain teams. You work across the major agentic AI stacks rather than a single vendor. Depending on the engagement, that means building with Anthropic, OpenAI, Google or Microsoft, and with vendor-neutral frameworks such as LangGraph where the customer wants to stay portable across models. You prove the idea quickly in a visual builder when that helps the customer. You then move to governed, code-first delivery, where evidence, tests, evaluation and release confidence turn the prototype into verified value. You pick the right model, framework and harness for the customer’s constraints, then make it reliable in their environment. Success is measured by what the customer can run in production and the value it delivers, not by the demo. In the FDE Practice this role covers both value engineering and quality engineering. The value engineering emphasis is implementation and user value. The quality engineering emphasis is evaluation, reliability, release safeguards and production readiness. You may lean one way or the other depending on the engagement. You will use coding agents where they increase speed, but you stay responsible for direction, review and control. You supervise agent output, inspect assumptions, write or refine code and hold the line on enterprise delivery standards.
Key Responsibilities
What you will do • Build agentic AI solutions: single and multi-agent systems, tool use, retrieval pipelines, automation and prototypes that reach production. • Build on the major agentic stacks, including the Claude Agent SDK, OpenAI Agents SDK, Google Agent Development Kit, Microsoft Agent Framework and vendor neutral frameworks such as LangGraph, choosing the right one for the customer’s needs. • Work with customer technical teams to understand systems, constraints, data, deployment paths and operating needs. • Use coding agents and human engineering judgement to move quickly while preserving review, test and release discipline. • Integrate agents with enterprise systems, APIs, cloud services, databases, ticketing systems, model services and security controls, often through MCP and similar tooling. • Implement agent evaluations, automated tests, observability hooks, runbooks and release safeguards. • Troubleshoot implementation, deployment, data, model and integration issues. • Codify what works into reusable patterns, starter kits and playbooks, alongside technical decisions, defects, risks and lessons learned, to raise the floor for the wider practice. • Feed field insight back to the platform vendors through our partnerships, turning recurring friction and gaps into product feedback that shapes the tools you build on. • Support handover and production readiness with clear documentation and operational ownership. • Work with architects and delivery managers to keep implementation aligned to value and technical guardrails.
What we are looking for These are the essentials. If you meet most of them, we want to hear from you. • Several years of production software engineering experience in Python, TypeScript, Java or comparable stacks. • Hands-on experience building with LLMs and agents on at least one major platform, such as Anthropic, OpenAI, Google or Microsoft. • Practical understanding of agentic patterns: tool use, retrieval, prompting, model behaviour, evaluation and multi-agent workflows such as orchestration, delegation and self-reflection. • Experience with APIs, databases, cloud platforms, integration surfaces or comparable deployment environments. • Strong debugging, testing and integration discipline. • Experience working in ambiguous delivery contexts with customers, users or cross functional technical teams. • Ability to balance fast prototyping with production-quality engineering.
Skill Requirements
Nice to have These would strengthen your application, but they are not deal-breakers. We do not expect every candidate to bring all of them. • Experience taking agentic or AI systems from prototype to production rollout. • Experience with agent orchestration frameworks, MCP servers or custom tool integrations. • Experience with data and retrieval pipelines, workflow automation or model deployment. • Experience with evaluation, observability, troubleshooting or release safeguards for AI systems. • A vendor certification on one of the major AI platforms (Anthropic, OpenAI, Google or Microsoft).
Other Requirements
Who does well here The strongest people in this role are: • Hands-on and pragmatic, with a bias towards working software. • Careful about quality, testing, integration and operational consequences. • Comfortable pairing with customers, architects, engineers and agents. • Quick to learn unfamiliar domains, APIs and systems. • Clear in writing, especially around decisions, assumptions, tests and handover notes. • Willing to challenge generated code and weak assumptions rather than accept output at face value. • Focused on user value without ignoring maintainability and production risk.
#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply-#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply-