AI/ML Engineer

We are looking for an experienced AI/ML Engineer to design, build, and deploy scalable machine learning systems for real-world applications. This role requires a strong foundation in traditional machine learning, along with exposure to modern AI approaches such as NLP, Generative AI, and time series forecasting.

You will work closely with cross-functional teams to develop end-to-end ML pipelines, productionize models, and contribute to data-driven decision-making across business functions.

• Design and develop end-to-end ML pipelines for scalable and production-ready systems
• Build and deploy machine learning models across use cases including regression, classification, ensemble methods, deep learning, and time series forecasting
• Develop and optimize time series models for forecasting use cases such as demand prediction, trend analysis, or anomaly detection
• Productionize ML models and ensure reliability using MLOps frameworks and best practices
• Work with structured and unstructured data using SQL and database systems
• Collaborate with stakeholders to translate business requirements into ML solutions
• Optimize models for performance, scalability, and accuracy
• Work on NLP, LLMs, or Generative AI-based use cases where applicable
• Create dashboards and insights using tools like Power BI, Tableau, or similar

• 5+ years of experience in AI / ML roles
• Strong proficiency in Python and ML libraries
• Hands-on experience building and deploying ML pipelines and scalable AI systems
• Solid understanding of machine learning algorithms (regression, classification, ensemble methods, deep learning)
• Experience with time series forecasting techniques (ARIMA, Prophet, LSTM, etc.)
• Experience in model productionization and MLOps platforms (e.g., Azure ML, DataRobot, Abacus.ai)
• Strong knowledge of SQL and database structures
• Experience with data visualization tools (Power BI, Tableau, etc.)

• Experience with NLP, Large Language Models (LLMs), or Generative AI
• Familiarity with cloud platforms such as AWS, Google Cloud, or Azure
• Experience working on real-world forecasting problems (finance, supply chain, user behavior, etc.)