Do you want to help shape the future of AI-powered work? At Avanade, you'll have the opportunity to design, build, and deploy cutting-edge Generative AI and Agentic AI solutions using the latest Microsoft technologies.

We are seeking an innovative and hands-on AI Engineer to join our Data & AI practice. In this role, you will work alongside clients and multidisciplinary teams to develop intelligent solutions that solve real business challenges using Large Language Models (LLMs), AI agents, automation, and Microsoft AI platforms.

The ideal candidate combines strong software engineering fundamentals with a passion for emerging AI technologies and a desire to continuously learn, experiment, and innovate.

You will join a vibrant community of AI specialists, architects, engineers, and consultants who are helping organisations transform how they work through AI.

About the Role

As an AI Engineer, you will design and develop AI-powered applications, copilots, and agentic solutions across the Microsoft ecosystem. You will work closely with clients to understand business challenges, identify opportunities for AI adoption, and translate requirements into secure, scalable, and production-ready solutions.

The role combines hands-on engineering, solution design, rapid prototyping, client-facing consulting, and continuous innovation.
You will contribute across the full solution lifecycle—from discovery and proof of concept through to deployment, evaluation, and optimisation.

Key Responsibilities:

In this role, you will:

•    Design, develop, and deploy AI-powered applications and agentic solutions using Microsoft AI technologies.

•    Build intelligent agents, copilots, and workflow automation solutions using Microsoft Copilot Studio, Microsoft 365 Copilot, Azure AI Foundry, and related services.

•    Develop APIs, integrations, and backend services using Python and modern application frameworks.

•    Create proof of concepts and prototypes to demonstrate emerging AI capabilities and business value.

•    Implement prompt engineering, orchestration, grounding, and retrieval techniques to improve AI performance and reliability.

•    Build and optimise Retrieval-Augmented Generation (RAG) solutions and knowledge retrieval experiences.

•    Contribute to AI solution architecture, technical design, development, testing, deployment, and documentation.

•    Participate in client workshops, discovery sessions, demonstrations, and technical discussions with both business and technical stakeholders.

•    Apply Responsible AI principles, governance controls, and evaluation frameworks throughout the AI development lifecycle.

•    Collaborate with architects, engineers, consultants, analysts, and client teams within agile delivery environments.

•    Stay current with emerging AI technologies, tools, frameworks, and industry trends, sharing knowledge across the wider practice.

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Priority Skill
Artificial Intelligence (AI)

Skills & Experience

We are looking for candidates with:

•    Strong software engineering experience with proficiency in Python.
•    Experience building APIs using frameworks such as FastAPI, Flask, or Django.
•    Knowledge of containerisation technologies such as Docker.
•    Hands-on experience with Large Language Models (LLMs), including OpenAI GPT models, Claude, Gemini, Llama, Mistral, or similar.
•    Experience applying prompt engineering techniques to improve model performance and user experience.
•    Understanding of AI solution evaluation, testing frameworks, and Responsible AI practices.
•    Familiarity with Microsoft's AI ecosystem, including Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Power Automate, and related services.
•    Strong understanding of software engineering principles, design patterns, security, and development best practices.
•    Experience working in agile teams and collaborating across multiple disciplines.
•    Strong communication skills with the ability to explain technical concepts to both technical and non-technical audiences.
•    Ability to engage directly with clients and understand business requirements.

Preferred Qualifications
Experience in one or more of the following areas would be advantageous:

•    Vector databases and semantic search technologies.
•    Retrieval-Augmented Generation (RAG) architectures and enterprise knowledge systems.
•    AI orchestration frameworks such as Semantic Kernel, LangChain, LangGraph, AutoGen, PromptFlow, Microsoft Agent Framework, or comparable technologies.
•    Agent communication standards and protocols such as Model Context Protocol (MCP) and Agent-to-Agent (A2A).
•    AI workflow automation platforms such as Power Automate, n8n, OpenAI Agent Builder, or similar tools.
•    Git-based development workflows and CI/CD pipelines.
•    Azure cloud services and modern application deployment practices.
•    Experience using AI coding assistants and developer productivity tools.
•    Azure AI Fundamentals (AI-900) or Azure AI Engineer Associate (AI-102) certification.
•    Personal projects, open-source contributions, research, or portfolio work involving AI agents, copilots, or LLM-driven applications.
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Qualifications
•    Bachelor's degree in Computer Science, Data Science, Software Engineering, Artificial Intelligence, or a related discipline.
•    Alternatively, equivalent practical experience and demonstrated technical capability will be considered.
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About You

You are someone who:

•    Is naturally curious and passionate about the future of AI and emerging technologies.
•    Enjoys experimenting, prototyping, and turning innovative ideas into practical solutions.
•    Takes ownership of deliverables and follows through from concept to completion.
•    Thrives in fast-moving environments where priorities and requirements evolve.
•    Enjoys learning new tools, frameworks, and techniques.
•    Balances technical excellence with a strong focus on client value and business outcomes.
•    Is a collaborative team player who enjoys working across diverse disciplines and perspectives.
•    Communicates confidently with stakeholders at all levels.
•    Actively contributes to knowledge sharing and continuous improvement within the wider AI community.

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