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.
- Strong 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.
- Strong 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