Gen AI Engineer

Interested candidates can share their resume to [email protected]

Job Summary

We are seeking a skilled Generative AI Engineer with 3-5 years of experience in designing and developing AI-powered applications. The ideal candidate should have hands-on expertise in Large Language Models (LLMs), LangChain, Retrieval-Augmented Generation (RAG), Python, and modern AI frameworks. The role involves building intelligent AI solutions, developing agent-based architectures, integrating enterprise data sources, and optimizing LLM performance for business use cases.

Key Responsibilities

• Design and develop Generative AI applications using LLMs.
• Build and optimize RAG-based solutions using vector databases and enterprise data sources.
• Develop AI workflows and agents using LangChain and related frameworks.
• Integrate OpenAI, Azure OpenAI, Claude, Llama, or other LLMs into business applications.
• Create prompt engineering strategies to improve response accuracy and model performance.
• Implement document ingestion, embeddings, semantic search, and vector storage solutions.
• Develop REST APIs and backend services using Python.
• Collaborate with cross-functional teams to deploy AI solutions into production environments.
• Monitor, evaluate, and improve AI model outputs for quality and scalability.

Required Skills

• 3-5 years of software development experience.
• Strong programming skills in Python.
• Hands-on experience with LangChain.
• Experience in building RAG (Retrieval-Augmented Generation) pipelines.
• Strong understanding of LLMs, Prompt Engineering, Fine-tuning, and AI Agents.
• Experience with Vector Databases such as Pinecone, ChromaDB, Weaviate, FAISS, or Milvus.
• Knowledge of OpenAI, Azure OpenAI, Anthropic Claude, Llama, or similar models.
• Experience with API development using FastAPI or Flask.
• Good understanding of embeddings, semantic search, and NLP concepts.
• Familiarity with Git and CI/CD practices.

Preferred Skills

• Experience with LangGraph, CrewAI, AutoGen, or Agentic AI frameworks.
• Exposure to cloud platforms such as AWS, Azure, or GCP.
• Knowledge of Docker and Kubernetes.
• Experience with ML frameworks such as PyTorch or TensorFlow.
• Understanding of MLOps and AI model deployment.