About the Role
We are looking for an experienced and innovative Lead Machine Learning Engineer – GenAI & LLMs for our MASIN AI vertical 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.
