Artificial Intelligence Engineer

GoML; United States; Remote; USD 120,000 - 160,000; Full time

Job summary

goML is seeking a Generative AI / Machine Learning Engineer to design, build, and productionize GenAI systems—from model training to deployment and monitoring. The role blends research with production engineering and offers ownership of end-to-end pipelines.

You will work on RAG, transformers, LLM-based architectures, and data pipelines, collaborating with senior engineers to solve real enterprise problems.

Qualifications

• Bachelor's or Master’s in Computer Science, ML, AI, or related field.
• 3–7 years of Generative AI/ML experience.
• Proficient in Python.
• Experience with RAG and LLM architectures.
• Experience building data pipelines and deploying ML/GenAI models.
• Strong ML/GenAI evaluation methods.
• Familiar with Git, Docker, Linux.
• Experience with AWS ML/GenAI services.

Responsibilities

• Design and build Generative AI solutions using RAG and transformers.
• Fine-tune pre-trained LLMs for domain tasks.
• Build and maintain data pipelines for training and inference.
• Apply software engineering practices to ML/GenAI pipelines.
• Monitor and evaluate model performance.
• Collaborate with cross-functional teams to scale GenAI solutions.

Skills

Generative AI

Python

RAG architectures

LLM architectures

Data pipelines

Production ML

ML evaluation

Git

Docker

AWS

Linux

Education

Bachelor's or Master’s in CS/ML/AI

Tools

Git

Docker

Linux

AWS

Job description

Build the Future of Generative AI with goML

At goML, were building the next generation of Machine Learning platforms and Generative AI services that solve real-world enterprise problems. We work at the intersection of cutting-edge research and production-grade engineeringturning ideas into scalable, impactful AI systems.Were looking for a Generative AI / Machine Learning Engineer to join our core team of young hustlers. In this role, youll design, build, and productionize GenAI systemsfrom model training and fine-tuning to deployment and monitoring. If youre excited about shaping how AI is built, scaled, and delivered, this is the place for you.

Why You? Why Now?

Generative AI is moving fast—from experimentation to enterprise adoption. We need engineers who can bridge research and production, build reliable ML pipelines, and turn GenAI breakthroughs into real business outcomes. This role is perfect for someone who enjoys ownership, experimentation, and solving complex problems end to end.

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
• 3–7 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