AI Engineer (LLM / GenAI)

Key Responsibilities: Solution Architecture & Deployment: Design and deploy secure, scalable GenAI architectures integrated into applications Build and deploy REST APIs for AI/ML models Work with Docker, Kubernetes in cloud environments (AWS/Azure/GCP) GenAI & LLM Development: Fine-tune and optimize LLMs (GPT, VAEs, GANs, transformer-based models) Implement RAG pipelines, embedding, and prompt engineering techniques Work with commercial and open-source LLMs (GPT, Claude, LLaMA, Phi) Agentic AI Development: Build and deploy AI agents using LangChain, LangGraph, CrewAI, Autogen, AgentFlow Implement multi-agent systems, orchestration, tool integration, and state management Develop autonomous or semi-autonomous workflows for business use cases MLOps & Optimization: Set up end-to-end MLOps pipelines (CI/CD, monitoring, retraining) Optimize performance, scalability, and infrastructure costs Use tools like Git, Docker, Kubernetes, vector databases Application Development & Data Integration: Develop APIs using FastAPI / Node.js Work with React, TypeScript, async patterns, WebSockets/SSE Handle data integration using REST APIs, SQL, and external systems Cross-Functional Collaboration: Partner with Engineering, Product, and Data teams Communicate complex AI concepts clearly to technical and non-technical stakeholders Stay updated with the latest advancements in GenAI and AI agents Required Skills: Strong proficiency in Python, SQL, and GenAI frameworks (e.g., LangChain) Hands-on experience with LLMs, RAG, embedding, and prompt tuning Experience building AI agents and multi-agent systems Experience with cloud platforms (AWS/Azure/GCP) and containerization Strong knowledge of REST APIs and data integration Experience with FastAPI, Node.js, React, TypeScript Understanding of MLOps and deployment practices Strong analytical, problem-solving, and communication skills Preferred: 4+ years of experience with GenAI/LLMs in production Experience with agent orchestration frameworks (CrewAI, LangGraph, Autogen) Exposure to client-facing AI solutions or cross-functional projects Open-source contributions, research, or AI project portfolio Requirements Strong proficiency in Python, SQL, and GenAI frameworks (e.g., LangChain) Hands-on experience with LLMs, RAG, embedding, and prompt tuning Experience building AI agents and multi-agent systems Experience with cloud platforms (AWS/Azure/GCP) and containerization Strong knowledge of REST APIs and data integration Experience with FastAPI, Node.js, React, TypeScript Understanding of MLOps and deployment practices Strong analytical, problem-solving, and communication skills Benefits Competitive salary and performance-based bonuses. Comprehensive insurance plans. Collaborative and supportive work environment Chance to learn and grow with a talented team. A positive and fun work environment 4 + years GenAI, Large Language Models (LLMs), GPT, RAG (Retrieval Augmented Generation), Prompt Engineering, AI Agents, LangChain, LangGraph, CrewAI, Autogen, AgentFlow, Python, SQL, REST APIs, FastAPI, Docker, Kubernetes, AWS, Azure, GCP, Vector Databases, Embeddings, Transformer Models, MLOps, CI/CD, Model Deployment, AI Orchestration, Multi-Agent Systems, State Management, WebSockets, Node.js, React, TypeScript Key Responsibilities: Solution Architecture & Deployment: Design and deploy secure, scalable GenAI architectures integrated into applications Build and deploy REST APIs for AI/ML models Work with Docker, Kubernetes in cloud environments (AWS/Azure/GCP) GenAI & LLM Development: Fine-tune and optimize LLMs (GPT, VAEs, GANs, transformer-based models) Implement RAG pipelines, embedding, and prompt engineering techniques Work with commercial and open-source LLMs (GPT, Claude, LLaMA, Phi) Agentic AI Development: Build and deploy AI agents using LangChain, LangGraph, CrewAI, Autogen, AgentFlow Implement multi-agent systems, orchestration, tool integration, and state management Develop autonomous or semi-autonomous workflows for business use cases MLOps & Optimization: Set up end-to-end MLOps pipelines (CI/CD, monitoring, retraining) Optimize performance, scalability, and infrastructure costs Use tools like Git, Docker, Kubernetes, vector databases Application Development & Data Integration: Develop APIs using FastAPI / Node.js Work with React, TypeScript, async patterns, WebSockets/SSE Handle data integration using REST APIs, SQL, and external systems Cross-Functional Collaboration: Partner with Engineering, Product, and Data teams Communicate complex AI concepts clearly to technical and non-technical stakeholders Stay updated with the latest advancements in GenAI and AI agents Required Skills: Strong proficiency in Python, SQL, and GenAI frameworks (e.g., LangChain) Hands-on experience with LLMs, RAG, embedding, and prompt tuning Experience building AI agents and multi-agent systems Experience with cloud platforms (AWS/Azure/GCP) and containerization Strong knowledge of REST APIs and data integrat