- 8+ years of experience.

- At least 5+ years hands on experience.

- Proficiency in deep learning frameworks (TensorFlow, PyTorch).

- Deep understanding of AI/ML algorithms and their applications.

- Have experience on handling Data Lake and ADB/PySpark data processing.

- Expertise in system design and architecture patterns.

- Expertise in End-to-end AI solution delivery.

- Knowledge of cloud-native AI solutions and microservices.

- Familiarity with AI ethics, bias mitigation, and explainability.

Key Responsibilities :

- Design, develop, and deploy machine learning and deep learning models for real-world applications.

- Work with cross-functional teams (data engineers, data scientists, domain experts) to define AI-driven solutions.

- Perform data preprocessing, feature engineering, model training, and evaluation.

- Implement end-to-end MLOps pipelines for scalable model deployment and monitoring.

- Use frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face for model development.

- Optimize models for performance, scalability, and accuracy.

- Integrate AI solutions with existing systems and APIs.

- Stay current with emerging AI/ML technologies and research trends.

Technical Skills Required :

- Programming : Python (mandatory), R or Java (optional)

- ML Frameworks : TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost

- Deep Learning : CNN, RNN, LSTM, Transformers, NLP models (BERT/GPT)

- MLOps Tools : MLflow, Kubeflow, Airflow, Docker, Kubernetes

- Data Handling : SQL, Pandas, NumPy, PySpark, data preprocessing

- Cloud Platforms : AWS Sagemaker / Azure ML / Google Cloud AI Platform

- Version Control : Git / GitHub

- APIs : RESTful API development, model serving using Flask/FastAPI