8 Genai Developer Jobs

Hiring: Generative AI Integration Developer (2+ yrs) – Hyderabad Work on LLM/GenAI integrations, Python APIs, prompt engineering & AI agent development. Strong Python, LangChain & FastAPI experience required. Required Candidate profile AI engineer with expertise in LLM APIs, Python, FastAPI, LangChain, and prompt engineering. Strong in integrating GenAI models, building AI agents, and deploying cloud-based microservices.

We are seeking a highly skilled Backend and GenAI Developer who is passionate about coding, problem-solving, and building cutting-edge AI-powered systems. Key Responsibilities: Develop, maintain, and optimize RESTful APIs using FastAPI . Implement authentication/authorization mechanisms including OAuth2, JWT, API security, middleware, caching, and rate limiting. Write clean, modular, scalable, and well-documented Python backend code . Integrate with external APIs, third-party services, and internal systems. Work with LangChain, LangGraph, MCP to build AI pipelines, agent frameworks, and RAG (Retrieval-Augmented Generation) systems. Design and manage database schemas in PostgreSQL, MongoDB, R...

Expertise in LLMs, multi-ai-agentic systems, and AI reasoning frameworks. Hands-on experience with Langchain, LangGraph, CrewAI, and Google Gemini Agent Builder. Strong programming skills in Python or Java for AI development. Familiarity with ReAct prompting, Chain-of-Thought reasoning, and AI fine-tuning. Experience with Google Cloud, Vertex AI, and CI/CD automation. Ability to evaluate AI model performance, scalability, and efficiency. Knowledge of multi-agent coordination, reinforcement learning, and distributed AI. Ability to evaluate models and write evaluation code Strong problem-solving skills for AI system optimization and troubleshooting Location- Pune, Chennai, Delhi, Bangalore, Ko...

About the Role: Were looking for a Senior AI-Powered Engineer with a strong background in Python, TypeScript, AWS, and hands-on experience working with Large Language Models (LLMs). In this role, youll build intelligent, production-grade applications that integrate LLMs and AI services to solve real-world business problems. You will work across the stack, from model integration and orchestration to backend systems and cloud deployment. Responsibilities: Design and develop AI-driven applications using LLMs (e.g., OpenAI, Anthropic, Hugging Face, etc.). Build and deploy Python- and TypeScript-based services and APIs that integrate with AI models. Optimize model performance, prompt engineering,...

GenAI: Hands-on integrating AI APIs (OpenAI/Azure OpenAI/AWS Bedrock), building RAG pipelines, prompt engineering, model fine-tuning, and working with vector DBs/semantic search. LLM app frameworks & orchestration: hands-on with LangChain and LangGraph for production agentic workflows. Backend: Strong in at least onePython (FastAPI/Flask/Django) or Node.js (Express) or Java (Spring Boot) or C# (.NET). Frontend: Proficiency with modern JS frameworks (React/Angular preferred), responsive design, and UX principles. APIs & Integration: Strong experience with REST, GraphQL, and common integration patterns.

Proficiency in Python, LangChain, LangGraph and GenAI APIs. Hands-on experience with vector databases (Pinecone, FAISS, ChromaDB). Understanding of Agentic AI, LLMs, transformers, and orchestration frameworks. Exposure to cloud services (AWS/GCP/Azure) and CI/CD practices. Stay current with AI trends and toolsSourcing & Screening US profiles.

Proficiency in Python, LangChain, LangGraph and GenAI APIs. Hands-on experience with vector databases (Pinecone, FAISS, ChromaDB). Understanding of Agentic AI, LLMs, transformers, and orchestration frameworks. Exposure to cloud services (AWS/GCP/Azure) and CI/CD practices. Stay current with AI trends and toolsSourcing & Screening US profiles.

o Design and implement multi-agent AI systems and protocols (MCP, A2A) - based workflows to handle complex, multi-step tasks and autonomous decision-making processes, build conversational AI chatbots and intelligent agents to provide instant, context-aware responses to user queries. o Implement generative AI solutions using Large Language Models (LLMs) for tasks such as automated document generation, content creation, and intelligent text summarization. Also, finetuning custom models if required Own the RAG & Data Engineering Pipeline: o Design, build, and maintain the end-to-end data engineering pipelines (ingestion, preprocessing, chunking) that are foundational to our AI systems. o Implem...