10/09/2026 updated
100 % available
Senior GenAI & Python Backend Engineer | Agentic AI | RAG | LLM Systems | Technical Lead
Rajkot, India
Only remote
Master of Engineering (M.E.), Pandit Deendayal Petroleum University (PDPU), Gandhinagar, 2017AI AgentsGenAILangChainPython (Programming Language)Azure Machine LearningFlaskMulti-Agent SystemsPrompt EngineeringGenerative AIBackendApi GatewayArtificial Intelligence Markup Language (AIML)
Generative AI & LLM Engineering
Expertise in building production-grade Generative AI solutions using Azure OpenAI, OpenAI API, Anthropic Claude, and GPT-4. Proficiency in Prompt Engineering, Function Calling, and Tool Calling for enterprise-scale AI applications.
Agentic AI & Multi-Agent Systems
Deep knowledge of Agentic AI frameworks including LangGraph, LangChain, CrewAI, and AutoGen. Design and implementation of Multi-Agent Systems, Agent Orchestration, and MCP (Model Context Protocol) for complex AI workflows.
Python Backend Development
Strong command of Python backend technologies including FastAPI, Django, Flask, SQLAlchemy, AsyncIO, REST APIs, WebSockets, and Server-Sent Events (SSE). Architecture of event-driven and microservices-based systems.
RAG & Vector Search
Implementation of Retrieval-Augmented Generation (RAG) pipelines using Embeddings, Vector Search, Pinecone, Azure AI Search, FAISS, and Chroma DB for intelligent information retrieval and grounded response generation.
Cloud Platforms (Azure & AWS)
Hands-on experience with Azure (AKS, Azure Functions, Service Bus, Cosmos DB, Blob Storage, Application Insights) and AWS (Lambda, S3, EC2, DynamoDB, API Gateway, CloudWatch, RDS) for cloud-native deployments.
DevOps & Security
Proficiency in Docker, Kubernetes (AKS), CI/CD pipelines, GitHub Actions, Jenkins, Azure DevOps, OAuth2, JWT, and RBAC for secure and automated software delivery.
LLMOps & Observability
Experience with LLM monitoring, guardrails, and observability tools including Azure Application Insights, LangSmith, and Guardrails for production AI system reliability.
Databases
Proficiency in working with PostgreSQL, MongoDB, Redis, Cosmos DB, and ElastiCache for scalable and high-performance data storage solutions.
MLOps
Implementation of MLOps workflows for predictive maintenance and machine monitoring, including automated testing with Pytest/TDD achieving high code coverage.
Expertise in building production-grade Generative AI solutions using Azure OpenAI, OpenAI API, Anthropic Claude, and GPT-4. Proficiency in Prompt Engineering, Function Calling, and Tool Calling for enterprise-scale AI applications.
Agentic AI & Multi-Agent Systems
Deep knowledge of Agentic AI frameworks including LangGraph, LangChain, CrewAI, and AutoGen. Design and implementation of Multi-Agent Systems, Agent Orchestration, and MCP (Model Context Protocol) for complex AI workflows.
Python Backend Development
Strong command of Python backend technologies including FastAPI, Django, Flask, SQLAlchemy, AsyncIO, REST APIs, WebSockets, and Server-Sent Events (SSE). Architecture of event-driven and microservices-based systems.
RAG & Vector Search
Implementation of Retrieval-Augmented Generation (RAG) pipelines using Embeddings, Vector Search, Pinecone, Azure AI Search, FAISS, and Chroma DB for intelligent information retrieval and grounded response generation.
Cloud Platforms (Azure & AWS)
Hands-on experience with Azure (AKS, Azure Functions, Service Bus, Cosmos DB, Blob Storage, Application Insights) and AWS (Lambda, S3, EC2, DynamoDB, API Gateway, CloudWatch, RDS) for cloud-native deployments.
DevOps & Security
Proficiency in Docker, Kubernetes (AKS), CI/CD pipelines, GitHub Actions, Jenkins, Azure DevOps, OAuth2, JWT, and RBAC for secure and automated software delivery.
LLMOps & Observability
Experience with LLM monitoring, guardrails, and observability tools including Azure Application Insights, LangSmith, and Guardrails for production AI system reliability.
Databases
Proficiency in working with PostgreSQL, MongoDB, Redis, Cosmos DB, and ElastiCache for scalable and high-performance data storage solutions.
MLOps
Implementation of MLOps workflows for predictive maintenance and machine monitoring, including automated testing with Pytest/TDD achieving high code coverage.
Languages
EnglishFluent
Project history
Architected an enterprise AI email-automation platform using FastAPI and Azure OpenAI (GPT-4), processing 100K+ emails/month with 90% automation and 95% CSAT. Designed and implemented a production Multi-Agent RAG workflow using LangChain, LangGraph and Pinecone for intelligent email classification, routing and grounded response generation. Built event-driven, real-time microservices using FastAPI, SSE and Azure Service Bus, containerized with Docker and Kubernetes (AKS). Integrated MCP (Model Context Protocol) for agent-to-tool interoperability and implemented LLM monitoring, guardrails, OAuth2/JWT and RBAC, while leading and mentoring a 5+ engineer team.
Built an AI-assisted documentation platform using Python, Django/FastAPI and OpenAI APIs for automated content generation and semantic search. Developed REST APIs and backend services using FastAPI/Django with PostgreSQL and Redis, deployed on AWS using Lambda, S3 and RDS. Containerized backend services with Docker and implemented CI/CD workflows to automate build, test and deployment processes.
Built a high-performance asynchronous Python backend using Flask, SQLAlchemy and AsyncIO for real-time industrial pump monitoring and equipment alerts. Implemented MLOps workflows for predictive maintenance, supporting machine-monitoring and operational alerting use cases. Developed real-time equipment surveillance using Flask-SocketIO and established automated testing with Pytest/TDD, achieving 90%+ code coverage. Containerized applications with Docker and implemented CI/CD automation for consistent development and production deployments.