AI / Machine Learning Engineer

About this role

Design, build, and productionize Generative AI systems at GoML — from model training and fine-tuning to deployment and monitoring on real enterprise problems.

About the role Build the next generation of Machine Learning platforms and Generative AI services that solve real-world enterprise problems. Design, build, and productionize GenAI systems — from model training and fine-tuning to deployment and monitoring. First 30 days: Foundation & immersion • Understand GoML’s ML and GenAI platforms, use cases, and architecture. • Get familiar with existing training, inference, and deployment pipelines. • Study current approaches to RAG, LLM fine-tuning, and model evaluation. • Collaborate with senior engineers and product teams to understand business problems. First 60 days: Build & experiment • Design and develop Generative AI solutions using RAG, transformers, and LLM-based architectures. • Fine-tune pre-trained LLMs for domain-specific and task-specific use cases. • Build and maintain data pipelines for training and inference workflows. • Apply strong software engineering practices to ML and GenAI pipelines. • Evaluate, analyze, and benchmark model performance and quality. • Develop and deploy proof-of-concept GenAI systems. First 180 days: Ownership & scale • Own end-to-end ML/GenAI pipelines — from training to production deployment. • Implement model optimization and compression techniques where applicable. • Productionize ML and GenAI research for real-world enterprise use cases. • Monitor deployed models and continuously improve performance and reliability. • Stay current with advancements in Generative AI and apply them thoughtfully. • Collaborate cross-functionally to solve challenging business problems at scale. What they are looking for • Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, or a related field. • 3+ years of experience in Generative AI, Machine Learning, or related domains. • Strong programming skills in Python. • Hands-on experience with RAG and LLM-based architectures. • Experience building data pipelines, deploying ML/GenAI models, and maintaining them in production. • Solid understanding of ML/GenAI evaluation techniques. • Proficiency with Git, Docker, and Linux-based systems. • Experience working with cloud platforms, especially AWS ML/GenAI services. Nice to have • Exposure to model compression and optimization techniques. • Experience with popular ML/GenAI frameworks and tools. • Familiarity with MLOps practices and monitoring systems. • Experience working in fast-paced startup environments. Work mode Full-time, remote-first, India, with opportunities for in-person collaboration.