Senior ML Engineer

Role & responsibilities

• Design, develop, train, and evaluate Machine Learning and Deep Learning models.
• Apply feature engineering, model selection, hyperparameter tuning, and model optimization.
• Build and maintain production-grade AI/ML solutions.
• Develop scalable ML pipelines and implement MLOps practices.
• Deploy, monitor, and maintain ML models in cloud environments.
• Work with ML platforms such as Amazon SageMaker, Azure Machine Learning, or Vertex AI.
• Develop GenAI applications using LLMs, RAG, embeddings, and vector databases.
• Build AI agent workflows using LangGraph, CrewAI, or similar frameworks.
• Develop AI solutions involving Computer Vision where required.
• Build scalable APIs and AI services using Python and FastAPI.
• Collaborate with engineering and product teams to solve real-world business problems using AI.
• Write clean, maintainable, and well-tested code and contribute to code reviews and technical decisions.

Preferred candidate profile

• 25 years of hands-on experience in Machine Learning and Deep Learning.
• Strong Python programming and problem-solving skills.
• Experience building and deploying production-grade ML solutions.
• Strong understanding and practical experience with ML algorithms, feature engineering, model evaluation, and optimization.
• Experience with MLOps and cloud ML platforms.
• Exposure to GenAI, LLMs, RAG, AI Agents, or Computer Vision is preferred.
• Good software engineering practices and ability to work independently in a startup environment.

Perks and benefits

• Opportunity to work on real-world AI/ML products.
• Exposure to ML, GenAI, AI Agents, and emerging AI technologies.
• Opportunity to work with modern cloud and MLOps platforms.
• Learning and growth opportunities in a fast-paced startup environment.
• Work closely with experienced AI and engineering teams.
• Opportunity to take ownership and make a direct impact on products.