The Role
Design, build, fine-tune, deploy, and monitor generative AI and machine learning systems. Develop RAG and LLM solutions, training and inference pipelines, model evaluation workflows, optimization techniques, and production-grade enterprise AI applications. Collaborate with engineering, product, and cross-functional teams while continuously improving model performance, reliability, and scalability.
Summary Generated by Built In
Build the Future of Generative AI with goML
What You’ll Do (Key Responsibilities)First 30 Days: Foundation & Immersion
At goML, 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 Generative AI / Machine Learning Engineer to join our core team of young hustlers. In this role, you’ll design, build, and productionize GenAI 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 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
- 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
- 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
- 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
- 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
- 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
- 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
Skills Required
- 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
- 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
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The Company
What We Do
Neuralgo.ai is an enterprise AI and data-science company building LLM-powered platforms that help users move from business problem statements to machine-learning solutions. Its offerings span data engineering, MLOps, and LLM applications, providing productized outcomes across the machine-learning lifecycle. The company’s mission is to democratize data science with AI and help organizations accelerate their machine-learning initiatives.
