- Build AI Solutions : Design and deploy Generative AI applications using LLMs, Prompt Engineering, and AI Agents.

- Orchestrate Workflows : Implement Agentic AI concepts including tool calling, workflow orchestration, guardrails, and human-in-the-loop design.

- Manage Knowledge Systems : Implement RAG (Retrieval-Augmented Generation), embeddings, and vector search systems.

- Engine Data Pipelines : Build scalable architectures to handle big data processing and complex data workflows.

- Collaborate Lifecycle : Work within Agile/DevOps frameworks to handle testing, release, and production support.

Required Technical Skills :

- AI & LLMs : Generative AI, Prompt Engineering, AI Agents, Responsible AI.

- Programming & Libraries : Advanced Python, Pandas, REST APIs, JSON, and automation workflows.

- Data Expertise : Data handling, Big Data architectures, and data processing.

- Frameworks : RAG, embeddings, vector databases, and enterprise knowledge systems.

- Tools : Jira, Azure DevOps, GitHub, Jenkins, Confluence, SharePoint, or Teams.

- Methodologies : SDLC, Agile delivery, DevOps, testing, and release management.

- Professional Skills Metrics : Ability to define and measure AI adoption, productivity, quality, and business value.

Soft Skills :

- Strong communication, stakeholder management, training, and problem-solving.