AI Engineer

Build the Future of Generative AI with goML

Why You? Why Now?

What You’ll Do (Key Responsibilities)

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 techniques like 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 You Bring (Qualifications & Skills)

Must-Have

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, or a related field
• 3–5 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

Who You Are

• A strong problem-solver with a research-driven yet pragmatic mindset
• Comfortable working independently and collaboratively
• Methodical, detail-oriented, and thoughtful in planning and execution
• A clear communicator who can explain complex ideas simply

Why Work With Us?

• Be part of a core team building next-gen ML & GenAI platforms
• Work on real enterprise problems, not just experiments
• High ownership, rapid learning, and strong growth opportunities
• Remote-first, with opportunities for in-person collaboration
• A culture built around curiosity, hustle, and impact