AI Engineer – Generative AI / LLM Engineer

Key Responsibilities

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.

Required Qualifications

 B.Tech / M.Tech  in Computer Science, Artificial Intelligence, Machine Learning, or related field.

6+ years of software engineering or AI/ML development experience.

Strong programming expertise in Python.

Hands-on experience with:

LLM fine-tuning

Transformer architectures

PyTorch / TensorFlow

Embedding models and semantic search

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.

Preferred Skills

Experience with multimodal AI systems

Experience deploying models on edge/mobile/embedded devices.

Exposure to Android/iOS AI deployment is advantageous.

Publications, open-source contributions, or Kaggle/AI competition experience are a plus