Job Opening: Junior Generative AI Engineer

Job Location: Bengaluru
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 Lang Chain, Llama Index, 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: Solid knowledge of Python.

- Generative AI: Basic understanding of LLMs, prompt engineering, and transformers.

- RAG: Understanding of document chunking, embeddings, retrieval, and vector search.

- Frameworks: Lang Chain, Llama Index, 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.

📌 Generative AI Engineer (India)
🏢 Evnek Technologies
📍 India