ML Engineer

Role & responsibilities

• Build, train, and evaluate ML/DL models.
• Perform feature engineering, model selection, and hyperparameter tuning.
• Develop and deploy production ML solutions and pipelines.
• Implement MLOps practices for model deployment and monitoring.
• Work with cloud ML platforms and AI/ML infrastructure.
• Contribute to GenAI, RAG, AI Agent, and Computer Vision projects.
• Develop Python-based APIs and AI services.
• Collaborate with the team to solve real-world business problems using AI.

Preferred candidate profile

• 13 years of hands-on experience in Machine Learning and Deep Learning.
• Strong Python programming and problem-solving skills.
• Good practical experience with ML algorithms, feature engineering, and model evaluation.
• Experience building ML models and applying them to real-world problems.
• Understanding of MLOps and model deployment.
• Exposure to GenAI, LLMs, RAG, AI Agents, or Computer Vision is preferred.
• Good understanding of software engineering practices and Git.
• Willingness to learn and work across different AI/ML technologies.

Perks and benefits

• Opportunity to work on real-world AI/ML products.
• Hands-on exposure to Classical ML, Deep Learning, GenAI, and AI Agents.
• Work with modern cloud and MLOps technologies.
• Learning and growth opportunities in a fast-paced AI startup.
• Opportunity to work closely with experienced AI/ML engineers.
• Scope to take ownership of projects and build production-grade solutions.