Lead AI/ML Engineer

About the Role

We are looking for an experienced and innovative Lead Machine Learning Engineer – GenAI & LLMs to drive the design, development, and deployment of cutting-edge AI solutions. In this role, you will lead the development of scalable machine learning systems powered by Large Language Models (LLMs), Generative AI, and Retrieval-Augmented Generation (RAG) frameworks.

Roles and Responsibilities 

• Lead the development and deployment of scalable ML systems using LLMs, GenAI models, and fine-tuned models like OpenAI and Gemini. 
• Build end-to-end ML pipelines for data ingestion, model training, fine-tuning, evaluation, RAG, and monitoring. 
• Fine-tune and integrate foundation models such as OpenAI's GPT, Gemini, LLaMA, Claude, and Mistral for specific use cases, including Retrieval-Augmented Generation (RAG). 
• Develop production-grade APIs and services using Python frameworks (FastAPI, Flask) with Docker, Kubernetes, and cloud platforms. 
• Implement LLMOps practices for model lifecycle management, including versioning, experiment tracking, and automated deployment. 
• Integrate vector databases (FAISS, Pinecone, Weaviate) with RAG systems for document retrieval and enhanced model performance. 
• Drive model performance improvements by optimising latency, accuracy, and hallucination rates, focusing on real-world applications and scalability. 
• Lead and mentor a team of ML engineers, ensuring high-quality code, best practices, and timely delivery of solutions. 
• Collaborate with backend, DevOps, legal, and product teams to ensure seamless integration of ML models and alignment with business goals. 
• Stay updated with the latest research in GenAI, LLMs, RAG, and emerging tools, and drive proof-of-concept development. 
• Ensure compliance with responsible AI practices, including data governance, privacy, security, and ethical considerations. 

Qualifications 

• Bachelor’s or master’s degree in computer science, Artificial Intelligence, Data Science, or a related field. 
• 6-8 years of experience in machine learning or NLP, with at least 1–2 years focused on GenAI/LLM systems and RAG. 
• Strong programming skills in Python and experience with ML frameworks like PyTorch, Scikit-learn, and TensorFlow. 
• Proficient with LLMs, including OpenAI's GPT, Gemini, LLaMA, and Mistral, as well as RAG systems. 
• Hands-on experience with fine-tuning and integrating OpenAI API, Gemini models, and RAG-based applications. 
• Experience working with vector databases (FAISS, Pinecone, Weaviate) and integrating them into machine learning workflows. 
• Knowledge of LLMOps tools and practices such as MLflow, LangSmith, Weights & Biases, or similar. 
• Expertise in deploying ML models using Docker, Kubernetes, and cloud platforms (AWS, GCP, Azure). 
• Strong understanding of model performance metrics, model observability, and post-deployment monitoring. 
• Proven leadership experience managing ML teams, driving innovation, and delivering production-grade ML systems.