🚀 Job Title: Generative AI Engineer (LLM | Python | AWS | FastAPI)📍 Location: Bangalore, India
5+ years of experience
💼 Employment Type: Permanent
💰 Budget: Up to ₹30 LPA
🕐 Notice Period: Immediate Joiners Preferred
About the RoleWe are seeking a highly skilled Generative AI Engineer with a strong foundation in Python, Large Language Models (LLMs), AWS, and FastAPI.
In this role, you will design, develop, and deploy scalable AI-driven systems and GenAI solutions that push the boundaries of automation, intelligent APIs, and AI-assisted decision-making.
This position offers a unique opportunity to work on cutting-edge GenAI applications, integrate LLMs into production systems, and collaborate with cross-functional teams to create next-generation AI capabilities.
Key Responsibilities- Design, fine-tune, and deploy Large Language Models (LLMs) for real-world use cases such as chatbots, text summarization, and knowledge retrieval.
- Develop end-to-end AI pipelines using Python and FastAPI, ensuring performance, scalability, and maintainability.
- Build and deploy API-driven GenAI services and integrate them into cloud-native environments (AWS preferred).
- Leverage AWS services (Lambda, S3, EC2, SageMaker, API Gateway) for scalable AI model hosting and automation.
- Collaborate with data scientists and MLOps engineers to improve model training, evaluation, and deployment pipelines.
- Implement prompt engineering, retrieval-augmented generation (RAG), and custom embeddings for enterprise-level AI applications.
- Ensure data security, version control, and model governance throughout the AI lifecycle.
- Conduct continuous performance optimization of AI systems and stay updated on the latest in Generative AI and LLM research.
Must-Have Skills- Programming: Expert in Python (OOPs, Async, API integration).
- Frameworks: FastAPI (must-have), Flask (good to have).
- AI/ML: Hands-on experience with LLMs, Prompt Engineering, LangChain, or RAG pipelines.
- Cloud: Proficiency in AWS (Lambda, SageMaker, EC2, S3, API Gateway).
- MLOps: Experience with model deployment, Docker, CI/CD, and API-based inference.
- Strong knowledge of NLP concepts, embeddings, and fine-tuning pre-trained transformer models (e.G., GPT, LLaMA, Falcon, Mistral).
Good to Have- Experience with Vector Databases (FAISS, Pinecone, Weaviate, or Chroma).
- Familiarity with OpenAI APIs, Hugging Face Transformers, and LangChain Framework.
- Exposure to frontend AI integrations (Streamlit, Gradio, etc.) for demo or prototyping.
- Understanding of Data Engineering workflows and API orchestration.