Generative AI Engineer

Location: Bangalore, India

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.