Solutions Architect - Backend (Agentic AI & Python)

We're looking for an experienced Agentic AI Engineer who has built and deployed production-grade AI applications using modern LLM frameworks. You'll design intelligent multi-agent systems, build scalable AI services, and work on cutting-edge Generative AI products.

Responsibilities:

• Design, develop, and deploy production-grade agentic AI applications.
• Build autonomous multi-agent workflows using LangGraph, LangChain, MCP, or similar frameworks.
• Develop RAG pipelines with vector databases for enterprise-scale AI applications.
• Design scalable Python/FastAPI backend services for AI platforms.
• Integrate OpenAI, Anthropic, Gemini, or open-source LLMs into production systems.
• Build tool-calling agents, orchestration pipelines, and structured AI workflows.
• Implement prompt engineering, evaluation frameworks, guardrails, and observability.
• Participate in architecture discussions, technical design reviews, and proof-of-concept initiatives.
• Mentor engineers, conduct code reviews, and drive engineering best practices.
• Collaborate with Product, Engineering, and Business teams to deliver AI solutions.

Requirements:

• Strong system design and architecture skills.
• Ability to independently own AI projects from design to production.
• Experience leading technical discussions and mentoring engineers.
• Excellent communication and stakeholder management skills.
• 7+ years of software development experience.
• Strong Python programming skills.
• Hands-on experience with LangChain and LangGraph.
• Experience building Agentic AI or Multi-Agent Systems.
• Strong understanding of RAG, embeddings, vector databases (Pinecone, Qdrant, ChromaDB, Weaviate, FAISS, etc. ).
• Experience with FastAPI, REST APIs, and microservices.
• Hands-on experience with OpenAI, Anthropic, Gemini, or similar LLMs.
• Experience with MCP (Model Context Protocol), tool calling, or AI orchestration frameworks.
• Good understanding of prompt engineering and LLM evaluation.
• Experience with cloud platforms (AWS/Azure/GCP) and Docker.

Good to Have:

• CrewAI, AutoGen, Google ADK, OpenAI Agents SDK, or similar frameworks.
• Kubernetes and CI/CD.
• LangSmith, LangFuse, Arize Phoenix, OpenInference, or AI observability tools.
• Experience with AI security, guardrails, and production deployment.