Key Responsibilities

Design, develop, and deploy ML models and AI solutions across various domains such as NLP, computer vision, recommendation systems, time-series forecasting, etc.

Perform data preprocessing, feature engineering, and model training using frameworks like TensorFlow, PyTorch, Scikit-learn, or similar.

Collaborate with cross-functional teams to understand business problems and translate them into AI/ML solutions.

Optimize models for performance, scalability, and reliability in production environments.

Integrate ML pipelines with production systems using tools like MLflow, Airflow, Docker, or Kubernetes.

Conduct rigorous model evaluation using metrics and validation techniques.

Stay up-to-date with state-of-the-art AI/ML research and apply findings to enhance existing systems.

Mentor junior engineers and contribute to best practices in ML engineering.

Required Skills & Qualifications

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.

4–8 years of hands-on experience in machine learning, deep learning, or applied AI.

Proficiency in Python and ML libraries/frameworks (e.g., Scikit-learn, TensorFlow, PyTorch, XGBoost).

Experience with data wrangling tools (Pandas, NumPy) and SQL/NoSQL databases.

Familiarity with cloud platforms (AWS, GCP, or Azure) and ML tools (SageMaker, Vertex AI, etc.).

Solid understanding of model deployment, monitoring, and CI/CD pipelines.

Strong problem-solving skills and the ability to communicate technical concepts clearly.