We re looking for an AI Engineer with hands-on experience in building, fine-tuning, and optimizing LLM-based applications. The ideal candidate will have solid expertise in RAG (Retrieval-Augmented Generation) architectures, parameter-efficient fine-tuning (e.g., LoRA), and model quantization techniques for deployment efficiency.

Key Responsibilities

- Design, implement, and optimize end-to-end LLM-based solutions for real-world applications.
- Develop and maintain RAG pipelines integrating vector databases, embeddings, and retrieval techniques.
- Fine-tune pre-trained language models using LoRA or similar methods.
- Apply quantization and optimization strategies to deploy models efficiently on constrained environments.
- Collaborate with data scientists, software engineers, and product teams to integrate AI features into production systems.
- Monitor, evaluate, and continuously improve model performance and reliability.

Required Skills





- 3 5 years of experience in AI/ML development or applied NLP.
- Proficient in Python and frameworks such as PyTorch or TensorFlow.
- Strong understanding of LLM architectures (e.g., GPT, Llama, Falcon, Mistral).
- Experience with RAG frameworks (LangChain, LlamaIndex, or custom retrieval setups).
- Hands-on knowledge of LoRA, PEFT, and model quantization (GPTQ, AWQ, or similar).
- Familiarity with vector databases like FAISS, Pinecone, or ChromaDB.
- Valuable understanding of prompt engineering and evaluation techniques.
- Cloud deployment experience (AWS, Azure, or GCP) is an advantage.

Preferred Skills

- Exposure to open-source models and fine-tuning pipelines.
- Experience integrating AI models into web or enterprise products.
- Knowledge of containerization and MLOps (Docker, Kubernetes, MLflow).

Experience - 3-5 Years

📌 AI Engineer (Bengaluru)
🏢 Gadgeon Smart Systems
📍 Bengaluru