AI/ML Engineer
Summary
Designs, trains, deploys, and monitors production ML models end to end, with a focus on time-series forecasting using Python, Scikit-learn, PyTorch, and TensorFlow. Bonus exposure to MLOps, cloud platforms (AWS/Azure/GCP), and GenAI/LLMs. On-site role in Hyderabad for a 5+ year engineer.
AI/ML Engineer
Location: Hyderabad, India
Department: Engineering
Experience: 5+ Yrs
Skills: TensorFlow, Python, AI/ML, PyTorch
- 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.
- 5-10 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.
- 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.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, Statistics, or a related field.
- 5–10 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.
- Competitive salary and performance-based bonuses.
- Comprehensive health insurance.
- Paid time off and flexible working hours.
- Professional development opportunities and support for continuing education. Udemy Learning Licenses to enhance skills and stay up-to-date with the latest technology.
