- Design, build, and deploy AI/ML solutions using Large Language Models (LLMs) and Generative AI technologies.

- Fine-tune open-source and proprietary foundation models for domain-specific use cases.

- Work on model optimization techniques including quantization, pruning, distillation, and efficient inference.

- Develop Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases.

- Optimize models for edge/on-device deployment on low-resource hardware.

- Collaborate with product, platform, and application teams to integrate AI capabilities into products.

- Evaluate model performance using appropriate benchmarks and metrics.

- Stay updated with the latest advancements in AI, GenAI, multimodal AI, and edge AI ecosystems.

- Mentor junior engineers and contribute to technical design discussions.

Essential Skills :

- Strong programming expertise in Python.

- Hands-on experience with :

1. LLM fine-tuning

2. Transformer architectures

3. PyTorch / TensorFlow

4. Embedding models and semantic search

5. Vector databases (FAISS, ChromaDB, Pinecone)

- Experience in model quantization and optimization techniques (INT8, 4-bit, GGUF, ONNX, TensorRT, llama.cpp, etc.

- Good understanding of RAG architectures and prompt engineering.

- Strong debugging, analytical, and problem-solving skills