Senior AI Platform Architect – Agentic SDLC Automation

Title :Senior AI Platform Architect – Agentic SDLC Automation

Location : London, UK - Hybrid 2 to 3 days

1. The Role

We are looking for an experienced AI Platform Architect to design, architect, build, and operationalise an enterprise Agentic AI Harness that enables end-to-end SDLC automation across .NET and Azure environments.

The successful candidate will define the architecture, standards, patterns, and operating model for an agent-based engineering platform scaffolding or harness using GitHub Copilot Agents and frameworks such as Microsoft Agent Framework, ADK, LangGraph, LangChain, and LangFuse. This harness will automate and assist key SDLC activities, including requirements generation, specification creation, story breakdown, architecture and design decision capture, task planning, test generation, code creation, test execution, infrastructure-as-code generation, and CI/CD pipeline creation.

The target outcome of the harness will be production-ready .NET applications deployable into Azure cloud environments.

In addition to designing and implementing the platform, the role will provide architectural leadership and guidance to architects and engineering teams, helping establish Agentic Software Delivery practices across the organisation.

This role is ideal for someone with strong software architecture experience, practical AI engineering skills, and a deep understanding of modern DevOps, cloud-native architecture, platform engineering, and enterprise-grade application delivery.

2. Tech Skills Required

The candidate should have hands-on experience with the following:

AI and Agentic Engineering

· Experience designing and building AI agents, multi-agent workflows, or agentic orchestration systems

· Practical knowledge of frameworks such as Microsoft Agent Framework, ADK, LangGraph, LangChain and LangFuse

· Prompt engineering, tool calling, structured outputs, agent memory, planning, routing, and evaluation patterns

· Experience integrating LLMs with enterprise systems, repositories, APIs, and development workflows

· Understanding of AI observability, tracing, evaluation, guardrails, and feedback loops

· Experience defining architecture patterns and governance for enterprise AI platforms

SDLC Automation and Developer Productivity

· Experience using AI coding tools such as GitHub Copilot, Claude Code and Cursor

· Ability to design automated workflows for:

o Requirements analysis

o Specification generation

o User story creation

o Architecture and design decision records

o Task breakdown

o Test case generation

o Test plan creation

o Code generation

o Test execution

o Documentation generation

· Experience defining engineering standards, reusable templates, and software delivery patterns

Software Architecture and Engineering

· Strong experience with .NET / C#

· Experience designing APIs, services, backend systems, and cloud-native applications

· Good understanding of software architecture, clean code principles, design patterns, secure coding practices, and Test-Driven Development (TDD) methodologies

· Experience with unit testing, integration testing, functional testing, and automated test execution

· Familiarity with test frameworks and automated testing tools used within the .NET ecosystem

· Experience producing architecture artefacts including reference architectures, architecture decision records, and implementation blueprints

Cloud and Platform Architecture

· Strong experience with Microsoft Azure

· Experience designing cloud-native platforms and developer enablement capabilities

· Understanding of Infrastructure-as-Code, CI/CD pipelines, platform engineering, and cloud governance

· Experience designing secure, scalable, and resilient enterprise solutions

3. What the Role Will Do

The AI Platform Architect will be responsible for defining, designing, and implementing an Agentic SDLC platform capable of supporting the full software delivery lifecycle.

Key Responsibilities

· Define the target-state architecture for an Agentic Software Delivery platform

· Design and build an AI Agentic Harness that orchestrates multiple AI agents across the SDLC

· Create reference architectures, reusable patterns, standards, and implementation blueprints for Agentic Engineering

· Use frameworks such as Microsoft Agent Framework, ADK, LangGraph, LangChain, and LangFuse to create agent workflows

· Integrate the harness with development tools such as GitHub Copilot, Claude Code, Cursor, GitHub, Azure DevOps, and CI/CD platforms

· Establish architectural guardrails, governance controls, and quality standards for AI-generated software delivery

· Build agents that can generate:

o Business and technical specifications

o Epics, features, and user stories

o Acceptance criteria

o Design decisions and architecture decision records

o Engineering tasks and implementation plans

o Test cases and test plans

o .NET application code

o Infrastructure-as-Code templates

o CI/CD pipeline templates

o Deployment documentation

· Execute the harness to generate working .NET / C# code

· Build validation loops to review generated outputs for correctness, security, quality, and maintainability

· Create test automation capabilities that generate and execute tests against produced code

· Implement mechanisms for code review, quality checks, static analysis, and automated feedback

· Ensure generated applications are deployable into Azure environments

· Create reusable patterns, templates, prompts, workflows, and agent configurations

· Implement observability and traceability for agent decisions, tool usage, prompts, responses, and generated artefacts

· Work with enterprise architects, solution architects, engineers, product owners, and platform teams to align the harness with enterprise engineering standards

· Train and mentor architects and development teams on Agentic Software Delivery approaches and AI-assisted engineering practices

· Contribute to security, compliance, governance, and responsible AI controls around AI-generated software delivery

· Continuously improve the platform based on output quality, developer feedback, and delivery outcomes

4. Nice to Have Skills

The following skills would be advantageous:

· Experience with Semantic Kernel, Azure AI Foundry, Azure OpenAI, or OpenAI APIs

· Experience with Model Context Protocol (MCP) tools and agent tool integration

· Experience creating reusable software factory, internal developer platform, or platform engineering capabilities

· Familiarity with DevSecOps, secure SDLC, and automated security testing

· Experience building RAG-based systems or integrating AI agents with enterprise knowledge bases

· Knowledge of enterprise data privacy, Responsible AI, and AI governance controls

· Experience working in Agile delivery environments

· Ability to create technical documentation, reference architectures, architecture standards, and engineering playbooks

· Experience coaching architects and engineering teams on modern software delivery practices and AI adoption