AI/ML Engineer

Location: Hyderabad, India

Department: Engineering

Experience: 4-10

AI/ML Engineer

About the Role

Key Responsibilities

• Design, develop, train, evaluate, and deploy machine learning models for real-world business use cases.
• Develop end-to-end ML pipelines, covering data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
• Perform data preprocessing, cleaning, transformation, feature engineering, and exploratory data analysis.
• Develop and optimize ML models using Python and frameworks such as Scikit-learn, PyTorch, and TensorFlow.
• Develop Time Series Forecasting models using techniques and frameworks such as Prophet and other forecasting approaches.
• Evaluate model performance using appropriate metrics and validation techniques.
• Perform model optimization and hyperparameter tuning to improve accuracy, performance, and scalability.
• Work with structured and unstructured datasets to identify patterns, trends, and insights.
• Build reusable Python-based ML components, pipelines, and applications.
• Deploy ML models into production environments and ensure their reliability and scalability.
• Implement ML model monitoring to track model performance, data quality, drift, and production issues.
• Collaborate with Data Scientists, Data Engineers, Software Engineers, and Product teams to translate business requirements into ML solutions.
• Troubleshoot model and pipeline issues and continuously improve production ML workflows.
• Follow software engineering best practices including version control, testing, documentation, and code reviews.

Required Skills

• 4-10 years of professional experience in AI/ML, Machine Learning Engineering, Data Science, or a related field.
• Strong programming experience in Python.
• Strong understanding of Machine Learning concepts and algorithms.
• Hands-on experience with machine learning frameworks/libraries such as:
  • Scikit-learn
  • PyTorch
  • TensorFlow
• Experience in Time Series Forecasting, preferably using Prophet or similar forecasting frameworks.
• Strong knowledge of feature engineering and data preparation techniques.
• Experience with model development, training, evaluation, deployment, and monitoring.
• Experience with model optimization and hyperparameter tuning.
• Good understanding of statistics, probability, and data analysis.
• Experience working with Pandas and NumPy.
• Good knowledge of SQL and working with structured data/databases.
• Strong analytical and problem-solving skills.

Good to Have

MLOps & Production Engineering

• Knowledge of MLOps practices and machine learning lifecycle management.
• Experience with CI/CD pipelines for ML applications.
• Experience with model versioning and experiment tracking.
• Experience deploying and managing ML models in production environments.
• Familiarity with tools such as MLflow or similar MLOps platforms.
• Experience with Docker/Kubernetes is an added advantage.

Cloud & Modern Data Platforms

• Experience working with cloud platforms such as AWS, Azure, or GCP.
• Exposure to modern cloud-based data platforms and data environments.
• Experience with cloud-based ML services or deployment environments is an advantage.

Generative AI & Emerging Technologies

• Experience with Generative AI and Large Language Models (LLMs).
• Knowledge of RAG (Retrieval-Augmented Generation) architectures.
• Experience with embeddings and vector databases.
• Understanding of prompt engineering.
• Exposure to Agentic AI / AI Agents and agent-based architectures.
• Experience integrating LLMs into production applications is an added advantage.

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, Statistics, or a related field.
• 4–10 years of relevant professional experience.
• Strong communication and collaboration skills.
• Ability to work independently and as part of a cross-functional team.
• Strong interest in learning and working with emerging AI/ML technologies.

What We Offer

• Opportunity to work on AI/ML and emerging technology initiatives.
• Exposure to real-world Machine Learning, Time Series Forecasting, and AI use cases.
• Opportunity to work with modern cloud, MLOps, and data technologies.
• Exposure to Generative AI, LLMs, RAG, and Agentic AI initiatives.
• Collaborative and learning-focused work environment.
• Career growth opportunities and exposure to innovative projects.