Application Developer - AI Technical Consultant - 9216

Job description

This approach combines AI-native ways of working, strong business understanding, and trust-led transformation. We are looking for an **AI Technical Consultant** who can design and build intelligent AI agents and client-facing accelerators. The candidate will work across: - Large Language Models - Agentic AI - Retrieval-Augmented Generation - APIs and microservices - Enterprise data sources - Cloud platforms - Consulting workflows The role requires strong technical development and client collaboration skills. The candidate should be able to understand a business problem and quickly convert it into a practical AI solution. The candidate will also be responsible for taking solutions from initial idea and prototype to deployment and operational support. **Role Objectives:** The AI Technical Consultant will: - Own and lead the development of AI agents used by consultants. - Build reusable AI accelerators for client engagements. - Convert business requirements into practical AI solutions. - Integrate AI agents with enterprise systems and data sources. - Support rapid prototyping and production-ready implementation. - Balance delivery speed, engineering quality, security, and business value. - Promote responsible and controlled use of AI. **Primary Skills:** - Python and FastAPI - TypeScript or Node.js - REST API and GraphQL development - Large Language Models - Agentic AI development - Retrieval-Augmented Generation - Prompt engineering - LangChain - Microsoft Agent Framework - OpenAI SDK - Azure AI SDK - Microsoft Copilot Studio - Vector databases - Enterprise system integration - Docker - Cloud platforms - Git and CI/CD **Mandatory Skills:** **AI and GenAI** - Hands-on experience in building LLM-based applications. - Strong understanding of AI agents and agent workflows. - Experience with prompt engineering and tool integration. - Good knowledge of RAG architecture. - Understanding of embeddings, chunking, retrieval, and grounding. - Knowledge of LLM limitations and evaluation methods. - Experience in implementing AI guardrails. - Understanding of responsible AI principles. **Programming and Backend Development** - Strong hands-on experience in Python. - Experience with FastAPI or a similar Python framework. - Experience with TypeScript or Node.js is preferred. - Strong API development experience. - Good knowledge of REST services. - Working knowledge of GraphQL. - Ability to write clean, reusable, and maintainable code. - Strong debugging and troubleshooting skills. **AI Frameworks and SDKs** Hands-on experience with one or more of the following: - LangChain - Microsoft Agent Framework - OpenAI SDK - Azure AI SDK - Microsoft Copilot Studio - equivalent LLM or agent frameworks **Vector Search and RAG Platforms** Experience with one or more of the following: - Azure AI Search - Qdrant - Pinecone - FAISS - equivalent vector search platforms **Enterprise Integration** - Experience integrating multiple applications and data sources. - Knowledge of authentication and authorisation. - Experience with APIs, databases, and enterprise platforms. - Ability to convert data and system capabilities into usable AI workflows. - Understanding of secure enterprise integration patterns. **Cloud and Deployment** - Experience with Azure, AWS, or GCP. - Hands-on experience with Docker. - Understanding of container-based deployments. - Familiarity with Git and CI/CD pipelines. - Experience working across development and production-like environments. - Basic understanding of application monitoring and observability. **Good-to-Have Skills** - Microsoft Copilot Studio. - Azure AI Foundry or Azure AI services. - Azure OpenAI. - Claude, Gemini, or open-source LLM integration. - Multi-agent architecture. - Model Context Protocol. - AI workflow orchestration. - Knowledge graph or GraphRAG exposure. - SQL and NoSQL database experience. - SharePoint and Microsoft 365 integration. - CRM integration experience. - Azure Functions or AWS Lambda. - Kubernetes exposure. - Infrastructure-as-Code knowledge. - Observability and monitoring tools. - LLMOps or MLOps experience. - AI evaluation frameworks. - Experience in consulting accelerators or internal productivity tools. - Basic user interface development knowledge. - Experience with React or another modern frontend framework. **Tools and Technology Stack:** **AI and Agent Platforms** - Microsoft Copilot Studio - Azure AI - Azure OpenAI - LangChain - Microsoft Agent Framework - OpenAI SDK - Anthropic Claude - Google Gemini - Llama **Programming and APIs** - Python - FastAPI - TypeScript - Node.js - REST APIs - GraphQL **RAG and Search** - Azure AI Search - Qdrant - Pinecone - FAISS - Embedding models - Hybrid search - Reranking models **Data and Enterprise Platforms** - SharePoint - CRM platforms - SQL databases - NoSQL databases - Enterprise APIs - Cloud storage **DevOps and Deployment** - Git - GitHub - Azure DevOps - GitHub Actions - Docker - Container registries - Azure, AWS, or GCP **Experience Requirements:** - **4-8 years** of relevant professional experience. - Experience in software engineering, AI/ML engineering, data engineering, or automation roles. - Proven experience in building scalable applications or enterprise tools. - Hands-on experience with LLM or AI-enabled systems. - Experience integrating multiple enterprise systems. - Experience with production or near-production AI solutions. - Experience in rapid prototyping and iterative development. - Experience working with both technical and non-technical stakeholders. A background in consulting, product development, or internal tooling will be an advantage. **Educational Qualification:** Bachelors or Masters degree in: - Computer Science - Information Technology - Artificial Intelligence - Data Science - Software Engineering - Information Systems - or a related discipline Strong practical experience can also be considered. **Soft Skills:** - Strong ownership and