Agentic AI Consultant – Strategy & Consulting

About the role

The Agentic AI Consultant will design, develop, and deploy enterprise‑grade AI solutions that combine generative models, multi‑agent systems, and orchestration frameworks. Working within a global strategy and consulting network, you will help clients improve business outcomes across digital and commerce ecosystems.

Key responsibilities

• Design and implement Agentic AI and multi‑agent systems using industry‑standard frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, and LangGraph.
• Develop orchestration pipelines that coordinate specialized AI agents and manage data, knowledge, and context flows.
• Integrate AI solutions with enterprise APIs, backend platforms, and cloud services.
• Prepare, validate, and manage data inputs for AI workflows, including prompt engineering and retrieval‑augmented generation.
• Deploy, monitor, and maintain AI applications on cloud platforms (Azure, GCP, AWS) using FastAPI and Databricks.
• Apply Responsible AI practices—governance, traceability, security, and observability.
• Create technical documentation, best‑practice guides, presentations, demos, and client‑facing materials.

Required profile

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Business Administration, or a related field.
• Minimum 2 years of experience designing, developing, and deploying Agentic or Generative AI solutions.
• Proven ability to build scalable multi‑agent systems and orchestration pipelines.
• Strong analytical and problem‑solving skills; ability to communicate complex AI concepts to both technical and non‑technical stakeholders.

Required skills

• Python and SQL programming.
• Generative AI, Agentic AI, Large Language Models (LLMs), Multi‑Agent Systems.
• Prompt engineering and orchestration pipeline design.
• API integration and cloud architecture (Azure, GCP, AWS).
• FastAPI, Databricks, CI/CD, containerization technologies.
• Frameworks: LangChain, LlamaIndex, CrewAI, AutoGen, LangGraph.
• Vector databases, Redis or similar in‑memory stores, retrieval‑augmented generation (RAG) architectures.