Senior Associate Gen AI Engineer

Role Overview: You are a Senior GenAI Engineer with strong backend roots, responsible for designing and developing high-performance AI systems. Your focus will be on building intelligent agents, RAG pipelines, and scalable backend systems to solve real-world problems. You will work on diverse projects, leveraging your expertise in Python, FastAPI, LangChain, and LangGraph to create autonomous systems. Key Responsibilities: - Backend Development: Design and develop high-performance, asynchronous RESTful APIs using Python and FastAPI. - GenAI Engineering: Build and deploy production-grade RAG pipelines and AI Agents with a focus on prompt engineering and context management. - Agentic Workflows: Design stateful, multi-step agent workflows using LangGraph to reason, plan, and execute tasks efficiently. - Integration: Integrate LLMs (OpenAI, Anthropic, Llama 3, etc.) with external tools, databases, and third-party APIs. - Data & Embeddings: Manage Vector Databases and optimize retrieval strategies for accuracy. - Deployment: Dockerize applications and deploy AI microservices on cloud platforms (AWS/Azure/GCP). - Client Collaboration: Interact with clients to understand requirements and demo AI solutions. - Fine-Tuning & Model Adaptation: Execute Supervised Fine-Tuning on open-source models using LoRA and Q-LoRA adapters. Qualifications Required: - Experience: 2 years of professional software engineering experience. - Core Language: Expert proficiency in Python, including asyncio, Pydantic, and type hinting. - Backend Frameworks: Strong experience with FastAPI (preferred) or Flask/Django. - Generative AI Stack: Hands-on experience with LangChain framework and building agents using LangGraph. - Databases: Experience with SQL (PostgreSQL) and at least one Vector Database. - Version Control: Proficient with Git. Note: No additional details about the company were provided in the job description. Role Overview: You are a Senior GenAI Engineer with strong backend roots, responsible for designing and developing high-performance AI systems. Your focus will be on building intelligent agents, RAG pipelines, and scalable backend systems to solve real-world problems. You will work on diverse projects, leveraging your expertise in Python, FastAPI, LangChain, and LangGraph to create autonomous systems. Key Responsibilities: - Backend Development: Design and develop high-performance, asynchronous RESTful APIs using Python and FastAPI. - GenAI Engineering: Build and deploy production-grade RAG pipelines and AI Agents with a focus on prompt engineering and context management. - Agentic Workflows: Design stateful, multi-step agent workflows using LangGraph to reason, plan, and execute tasks efficiently. - Integration: Integrate LLMs (OpenAI, Anthropic, Llama 3, etc.) with external tools, databases, and third-party APIs. - Data & Embeddings: Manage Vector Databases and optimize retrieval strategies for accuracy. - Deployment: Dockerize applications and deploy AI microservices on cloud platforms (AWS/Azure/GCP). - Client Collaboration: Interact with clients to understand requirements and demo AI solutions. - Fine-Tuning & Model Adaptation: Execute Supervised Fine-Tuning on open-source models using LoRA and Q-LoRA adapters. Qualifications Required: - Experience: 2 years of professional software engineering experience. - Core Language: Expert proficiency in Python, including asyncio, Pydantic, and type hinting. - Backend Frameworks: Strong experience with FastAPI (preferred) or Flask/Django. - Generative AI Stack: Hands-on experience with LangChain framework and building agents using LangGraph. - Databases: Experience with SQL (PostgreSQL) and at least one Vector Database. - Version Control: Proficient with Git. Note: No additional details about the company were provided in the job description.