Roles and Responsibilities
Design and develop end-to-end AI solutions using various technologies such as Azure OpenAI/AWS Bedrock, Python, and API integration.
Collaborate with clients to understand their business requirements and design customized AI solutions that meet their needs.
Develop expertise in emerging technologies like LangChain/LangGraph, RAG (Responsible AI Governance), Vector Databases, Prompt Engineering, LLMOps (Large Language Model Operations).
Ensure seamless integration of AI solutions with existing systems through DevOps practices and security measures.

Required Skills & Experience
12 to 15 years of experience in Enterprise Solution Architecture and software engineering.
Solid expertise in Enterprise Architecture and AI Solution Design.
Advanced proficiency in Python.
Hands-on experience with Azure OpenAI and/or AWS Bedrock.
Experience with LangChain, LangGraph, and AI agent frameworks.
Strong understanding of Retrieval-Augmented Generation (RAG) architectures.
Experience designing and implementing AI Agents and multi-agent workflows.
Hands-on experience with vector databases such as Pinecone, Azure AI Search, Weaviate, Milvus, Chroma, or Qdrant.
Robust knowledge of Prompt Engineering techniques and LLM optimization.




Experience implementing LLMOps practices, including deployment, monitoring, evaluation, versioning, and governance.
Expertise in API integration, microservices, and enterprise integration patterns.
Robust understanding of cloud platforms (Azure and/or AWS) and cloud-native architecture.
Experience with DevOps, CI/CD pipelines, Infrastructure as Code, and container technologies.
Knowledge of AI security, Responsible AI, data privacy, and governance best practices.
Experience in client-facing solutioning, architecture consulting, and technical leadership.
Proven experience preparing RFP/RFI responses, solution proposals, and effort estimations.
Experience establishing enterprise development standards using Claude Code and GitHub Copilot.

Preferred Qualifications
Experience with Model Context Protocol (MCP), Agent-to-Agent (A2A) communication, and AI orchestration platforms.
Knowledge of model evaluation frameworks and AI observability tools.
Experience with enterprise knowledge management and semantic search solutions.
Cloud certifications in Azure and/or AWS.
TOGAF or other Enterprise Architecture certifications.
Experience working in Agile delivery environments.

📌 Genai Solution Architect Pune
🏢 Orion Systems
📍 Pune