AI Solution Architect

Role

• Design and develop production-ready Generative AI, RAG, AI Agents, workflow automation, and enterprise AI solutions.
• Build end-to-end AI workflows using Python, LLMs, APIs, enterprise tools, and automation frameworks.

Responsibilities

• Develop AI Agents, RAG systems, and LLM-powered applications.
• Build AI workflows and business process automation.
• Implement tool/function calling, agent orchestration, memory, and human-in-the-loop workflows.
• Develop enterprise connectors and integrations.
• Integrate Outlook, Teams, SharePoint, Jira, Git, Slack, databases, and REST APIs.
• Build Python APIs and backend services.
• Implement AI testing, evaluation, monitoring, and observability.
• Work with architects and engineering teams to deliver scalable production solutions.

Required Skills

• Programming: Python, FastAPI, REST APIs, Async Programming, OOP, Git, Unit Testing.
• AI/LLM: Generative AI, LLMs, Prompt Engineering, Function Calling, Tool Calling.
• RAG: Embeddings, Vector Databases, Semantic/Hybrid Search, Reranking, Document Processing.
• Agents: AI Agents, Agentic Workflows, Planning, Reasoning, Memory, Tool Execution.
• Frameworks: LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI.
• LLMs: OpenAI, Azure OpenAI, Anthropic, Gemini, Open-Source LLMs.
• Integration: Microsoft Graph, Microsoft 365, Outlook, Teams, SharePoint, Jira, Git, Slack, REST APIs, OAuth2, Webhooks, MCP.
• Infrastructure: Docker, CI/CD, Cloud/Private AI, Ollama, llama.cpp, vLLM.