Ai engineer
GoML
About the role
About
Build the Future of Generative AI with go ML
At go ML, we’re 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 engineering—turning ideas into scalable, impactful AI systems.
We’re looking for a Senior AI / Machine Learning Engineer to join our core team of young hustlers. In this role, you’ll design, build, and productionize Gen AI systems—from model training and fine‑tuning to deployment and monitoring. If you’re 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 Gen AI 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 go ML’s ML and Gen AI 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 Gen AI pipelines
- Evaluate, analyze, and benchmark model performance and quality
- Develop and deploy proof‑of‑concept Gen AI systems
First 180 Days: Ownership & Scale
- Own end‑to‑end ML/Gen AI pipelines—from training to production deployment
- Implement model optimization and compression techniques where applicable
- Productionize ML and Gen AI 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+ 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/Gen AI models, and maintaining them in production
- Solid understanding of ML/Gen AI evaluation techniques
- Proficiency with Git, Docker, and Linux‑based systems
- Experience working with cloud platforms, especially AWS ML/Gen AI services
Nice‑to‑Have
- Exposure to model compression and optimization techniques
- Experience with popular ML/Gen AI 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 & Gen AI 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
Requirements
- Strong programming skills in Python
- Hands-on experience with RAG and LLM-based architectures
- Experience building data pipelines, deploying ML/Gen AI models, and maintaining them in production
- Solid understanding of ML/Gen AI evaluation techniques
- Proficiency with Git, Docker, and Linux-based systems
- Experience working with cloud platforms, especially AWS ML/Gen AI services
Responsibilities
- Understand go ML’s ML and Gen AI 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
- 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 Gen AI pipelines
- Evaluate, analyze, and benchmark model performance and quality
- Develop and deploy proof-of-concept Gen AI systems
- Own end-to-end ML/Gen AI pipelines—from training to production deployment
- Implement model optimization and compression techniques where applicable
- Productionize ML and Gen AI 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
Skills
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