Software Platform Engineer II (AI Engineer)

Job Responsibilities:

• Architect and deliver AI-driven solutions that address high-value business needs, including document interpretation, automated decision flows, feedback generation, dashboard creation and approval management.
• Lead the development of agent-based workflows using LLMs, retrieval pipelines and multi-agent orchestration frameworks.
• Set the architecture and engineering standards for employee-facing agents built on the Microsoft Agent Framework and Microsoft 365 Agent SDK, delivered through Microsoft Teams and Adaptive Cards.
• Build Composite AI architectures, combining language models, search, business rules, embeddings and analytics.
• Design, develop and deploy Generative AI solutions using large language models such as GPT, Llama, Claude and similar.
• Design and optimise context pipelines - chunking strategies, prompting structure, memory systems and vector retrieval mechanisms on Azure AI Search.
• Develop robust C#/.NET and Python backend components and API integrations supporting AI agents, Azure AI services and MCP-based tools.
• Own the Infrastructure as Code estate for AI workloads using Terraform, and the CI/CD and DevOps practices around it.
• Define the observability, telemetry and AI performance optimisation approach; drive improvements in response quality, grounding accuracy and cost efficiency.
• Advance the platform roadmap across agent orchestration, personalization, knowledge management and self-service capabilities.
• Evaluate new use cases, propose viable AI solutions, and guide stakeholders through feasibility and solution design.
• Ensure solutions align with responsible AI standards, quality benchmarks and enterprise governance requirements.
• Collaborate with architects, product managers, UX and business teams to align AI initiatives with enterprise goals.
• Mentor engineers, lead design and code reviews, and build a high-performing AI engineering team.
• Monitor system performance, optimise agent behaviours and evolve solutions post-deployment.

Knowledge, Skills and Abilities

EducationBachelor's degree or master's in computer science, Engineering, or related technical discipline.

Experience6-8+ years of software engineering experience, including 3-5+ years of AI engineering, ideally within large organizations or enterprise platforms.

Proven track record of leading AI or GenAI initiatives, from concept to deployment.

Experience owning enterprise agent platforms or Microsoft 365 / Teams application estates is strongly preferred.

Knowledge and skills (general and technical)Strong command of:

• Large Language Models and modern GenAI techniques
• Agentic design principles - tool-augmented agents, planners, multi-agent coordination
• Retrieval-based systems - embeddings, vector search, context assembly
• Proficiency in Python and C#/.NET, developing scalable backend components and APIs
• Hands-on expertise with Azure AI / Azure OpenAI, Azure Functions, APIM, storage and related cloud services
• Extensive experience with Microsoft Azure services and cloud architecture patterns
• Microservices architecture, API design and distributed systems
• CI/CD pipelines, automated testing and DevOps practices
• Mentoring engineers and building high-performing teams

Microsoft agent and enterprise stack

• C# and .NET application development
• Microsoft Agent Framework (preferred) or similar enterprise agent frameworks
• Microsoft 365 Agent SDK
• Azure AI Search, RAG architectures and knowledge retrieval pipelines
• Microsoft Teams applications, Adaptive Cards and conversational experiences
• REST APIs and enterprise system integrations
• Infrastructure as Code using Terraform
• Observability, telemetry and AI performance optimisation
• Designing AI solutions with a strong focus on usability, employee experience and response quality

Other Requirements (licenses, certifications, specialized training - if required)Experience with advanced Agentic frameworks:

• Microsoft Agent Framework / Microsoft 365 Agent SDK
• LangChain, LangGraph, Semantic Kernel, LlamaIndex, Hugging Face Transformers
• Azure AI Agent Services / Azure AI Foundry

Additional:

• Deep working knowledge of MCP (Model Context Protocol) and tool integrations.
• Vector search platforms (Azure AI Search, Pinecone, FAISS).
• Responsible AI, guardrails, evaluation frameworks and enterprise AI governance.
• Azure certification (AI-102 / AZ-305 / AZ-400) desirable.