Engineer - Gen AI

We are seeking an experienced Generative AI professional with expertise in designing and deploying Large Language Models (LLMs) from scratch as well as fine-tuning and optimizing existing models. The role requires deep technical skills, creativity, and hands-on experience in building scalable GenAI solutions across text, image, and multimodal domains.

Key Responsibilities:

• Design and build LLMs from scratch, including data pipeline creation, tokenization strategies, and training at scale.
• Fine-tune and optimize pre-trained models for specific enterprise use cases.
• Research, experiment with, and implement cutting-edge architectures in generative AI (transformers, diffusion, multimodal).
• Develop prompt engineering, RAG (Retrieval Augmented Generation), and knowledge integration pipelines.
• Collaborate with cross-functional teams to translate business problems into GenAI-powered solutions.
• Deploy and monitor models in production with focus on scalability, security, and performance.
• Ensure compliance with ethical AI practices, governance, and responsible AI usage.
• Document design, experiments, and best practices to contribute to organizational AI knowledge base.

Required Skills & Qualifications:

• Proven experience in LLM design and training from scratch, including data preparation, distributed training, and evaluation.
• Strong background in deep learning, NLP, and transformer architectures.
• Strong development experience in Python development and ML frameworks.
• Hands-on experience with vector databases (FAISS, Pinecone, Weaviate, Milvus) and RAG pipelines.
• Solid understanding of MLOps practices, containerization (Docker, Kubernetes).
• Must have Good Experience in AWS Cloud and its Services.
• Ability to optimize model inference, reduce latency, and manage large-scale deployments.
• Strong problem-solving, analytical, and communication skills.

Preferred Qualifications:

• Research or published work in LLMs or generative AI.
• Contributions to open-source AI frameworks or libraries.
• Exposure to multimodal AI (vision-language models, audio, video).
• Familiarity with AI regulation, compliance, and governance standards.