Generative AI Engineer

Job Opening: Junior Generative AI Engineer Job Location: Bengaluru (Preffer only Bengaluru based candidate) Experience: 0–1 Year Notice Period: Immediate Joiner About the Role We are looking for a passionate and enthusiastic AI Engineer with 0–1 year of experience to join our growing AI team. The ideal candidate should have a strong foundation in Python, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Generative AI technologies. Fresh graduates with hands-on AI/GenAI projects or relevant internship experience are encouraged to apply. Key Responsibilities Develop and implement AI-powered applications using LLMs and Generative AI. Build and optimize RAG pipelines for intelligent document retrieval and question-answering systems. Work with LLM frameworks such as LangChain, LlamaIndex, and Hugging Face. Integrate LLM APIs such as OpenAI, Azure OpenAI, or open-source models. Implement embeddings, semantic search, and vector databases. Develop Python-based APIs and backend services using FastAPI or Flask. Perform prompt engineering, LLM response evaluation, and model optimization. Collaborate with senior engineers to develop and deploy scalable AI solutions. Good to Have Exposure to AI agents, tool calling, and agentic AI workflows. Knowledge of fine-tuning techniques such as LoRA and PEFT. Familiarity with AWS, Azure, or GCP. Basic understanding of Docker and AI application deployment. Experience with academic or personal projects involving LLMs, RAG, or chatbots. Qualifications B.E./B.Tech/M.Tech/MCA in Computer Science, AI/ML, Data Science, or a related field. 0–1 year of relevant experience, including internships or project work. Strong problem-solving and analytical skills. Good communication skills and willingness to learn emerging AI technologies. Requirements Required Technical Skills Programming: Strong knowledge of Python. Generative AI: Basic understanding of LLMs, prompt engineering, and transformers. RAG: Understanding of document chunking, embeddings, retrieval, and vector search. Frameworks: LangChain, LlamaIndex, or Hugging Face. Vector Databases: FAISS, ChromaDB, Pinecone, or similar. API Integration: REST APIs and LLM API integration. Databases: Basic SQL and database concepts. Development Tools: Git, Jupyter Notebook, and VS Code.