Software Platform Engineer I

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.