AI Technical Consultant

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

Key Responsibilities:

1. AI Agent Design and Development

• Design and build intelligent AI agents for consulting and business workflows.
• Develop AI agents using platforms and frameworks such as:
  • Microsoft Copilot Studio
  • Azure AI
  • LangChain
  • Microsoft Agent Framework
  • OpenAI SDK
  • Claude
  • Gemini
  • Llama
• Build single-agent and multi-agent solutions based on business needs.
• Define agent goals, tasks, tools, memory, and decision logic.
• Develop reusable agent components and templates.
• Implement tool-calling and workflow execution.
• Ensure agents provide accurate, relevant, and controlled responses.
• Maintain human validation for important business decisions.

2. AI Accelerator Development

• Build reusable AI accelerators for client engagements.
• Develop tools that improve consultant productivity.
• Support AI accelerators for areas such as:
  • research and document analysis
  • proposal development
  • knowledge discovery
  • data analysis
  • business process support
  • decision support
  • project delivery automation
• Convert proof of concepts into scalable applications.
• Maintain reusable code, APIs, templates, and implementation guides.
• Ensure accelerators can be configured for different clients and use cases.

3. Prompt Engineering and AI Orchestration

• Design clear and effective prompts for different business use cases.
• Develop system prompts, task prompts, and reusable prompt templates.
• Build prompt chaining and workflow orchestration.
• Develop tool-calling and function-calling logic.
• Manage conversation context and memory.
• Implement structured output formats.
• Validate AI responses against required schemas.
• Improve prompt quality through regular testing and evaluation.
• Reduce incorrect or unsupported responses.

4. RAG Solution Development

• Design and implement Retrieval-Augmented Generation pipelines.
• Connect LLMs with enterprise documents and knowledge sources.
• Build document ingestion and preprocessing pipelines.
• Define suitable document chunking strategies.
• Generate, store, and manage embeddings.
• Work with vector search platforms such as:
  • Qdrant
  • Pinecone
  • FAISS
  • Azure AI Search
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