Generative AI Engineer

Generative AI Engineer

One of our enterprise AI clients is seeking a highly skilled and motivated Generative AI Engineer to design, develop, and deploy advanced AI solutions leveraging large language models, multimodal transformers, and generative architectures.

This is a senior-level, high-impact role focused on building production-grade AI systems across content generation, conversational AI, autonomous agents, and intelligent data synthesis use cases.

Key Responsibilities

• Develop and fine-tune large language models (such as GPT-class, LLaMA-family, Mistral-family, Claude-class models) for domain-specific downstream tasks
• Design and optimize Retrieval-Augmented Generation (RAG) pipelines using frameworks like LangChain, LlamaIndex, or Haystack
• Build end-to-end generative AI applications spanning text, code, image, and audio use cases
• Implement embedding-based retrieval systems using vector databases such as FAISS, Pinecone, Weaviate, or Qdrant
• Integrate foundation model APIs into scalable production workflows
• Work with multimodal models and generative systems for image, vision-language, and audio tasks
• Optimize model latency, throughput, and scalability for production environments
• Collaborate cross-functionally with ML engineers, data teams, and product stakeholders
• Stay current with advancements in generative AI research and apply them to real-world systems

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field
• Strong hands-on experience in ML/AI engineering, with demonstrated experience in generative AI or LLM-based systems
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
• Experience using HuggingFace Transformers, LangChain, or similar ecosystems
• Strong understanding of NLP, transformer architectures, embeddings, and deep learning fundamentals
• Experience building scalable ML pipelines and deploying models using Docker, Kubernetes, or similar infrastructure tools
• Familiarity with prompt engineering, fine-tuning strategies, evaluation methodologies, and production model monitoring

Key skills/competency

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Multimodal AI
• Python Programming
• Deep Learning Frameworks (PyTorch/TensorFlow)
• HuggingFace Transformers
• LangChain/LlamaIndex
• Vector Databases (Pinecone/Weaviate)
• MLOps (Docker/Kubernetes)
• Natural Language Processing (NLP)