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Open nowPosted 140 days ago

AI Engineer

GoML16 open roles

Where
Coimbatore
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Your applicationOpen nowAI EngineerGoML · Coimbatore

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 140 days ago

The posting

Build the Future of Generative AI with goML

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

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
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