Solutions Architect - Backend (Agentic AI & Python)

EngiNeo Solutions; Delhi; On-site; INR 3,000,000 - 5,400,000; Full time

Job summary

EngiNeo Solutions seeks an experienced Agentic AI Engineer to design, build, and deploy production-grade AI applications. You will craft autonomous multi-agent workflows, integrate diverse LLMs, and develop scalable backend services with Python and FastAPI.

You will mentor engineers, participate in architecture discussions, and collaborate with product and business teams to deliver cutting-edge AI solutions. Strong leadership and communication are essential.

Qualifications

• 7+ years of software development experience.
• Experience building agentic AI or multi-agent systems is required.
• Hands-on with LangChain, LangGraph, MCP, and RAG pipelines.
• Strong Python programming skills and cloud/docker familiarity.
• Ability to own AI projects from design to production and lead discussions.

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 PoC initiatives.
• Mentor engineers, conduct code reviews, and drive engineering best practices.
• Collaborate with Product, Engineering, and Business teams to deliver AI solutions.

Skills

System design

Project ownership

Mentoring engineers

Stakeholder management

Communication

Python

LangChain

LangGraph

MCP

LLM frameworks

Tools

LangChain

LangGraph

Pinecone

Qdrant

ChromaDB

Weaviate

FAISS

FastAPI

Docker

AWS

Job description

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.