AI/ML Data Scientist

Teliolabs Communication Private Limited

Key Responsibilities:

• Develop and Implement AI/ML Models for predictive analytics, automation, and intelligent decision-making.
• Analyze Large Datasets using statistical and machine learning techniques to extract valuable insights.
• Optimize and Scale ML Pipelines for real-time and batch processing in production environments.
• Deploy AI Models on Cloud Platforms such as AWS, GCP, or Azure using MLOps practices.
• Collaborate with Data Engineers to ensure seamless integration of AI/ML models into enterprise data platforms.
• Design and Optimize Feature Engineering techniques to enhance model performance.
• Implement Explainable AI (XAI) Practices to ensure model transparency and fairness.
• Ensure Data Quality and Integrity through rigorous data validation and preprocessing steps.
• Utilize Open-Source ML Libraries and Frameworks, including TensorFlow, PyTorch, Scikit-learn, and XGBoost.
• Develop and Maintain AI/ML Pipelines using tools such as Apache Airflow, MLflow, and Kubeflow.
• Research and Innovate new AI/ML methodologies to improve business processes and automation.
• Monitor Model Performance and Drift, ensuring continuous optimization and re-training.
• Support Data Science Initiatives by mentoring junior data scientists and collaborating with cross-functional teams.

Required Skills & Qualifications:

• 10+ years of experience in data science, machine learning, and AI model development.
• Strong Expertise in Machine Learning Algorithms, including supervised, unsupervised, and reinforcement learning.
• Proficiency in Programming Languages such as Python, SQL.
• Hands-on Experience with ML Frameworks, Open Source libraries such as TensorFlow, PyTorch, Scikit-learn, and XGBoost.
• Experience with Cloud AI Services, such as AWS SageMaker, and platforms such as Data bricks, Snowflakes.
• Deep Knowledge of MLOps Practices, including CI/CD for ML, model versioning, and monitoring.
• Strong Understanding of Data Technologies, including Spark and distributed computing.
• Experience in Natural Language Processing (NLP), CNN , and Deep Learning Techniques.
• Familiarity with AI Ethics, Bias Mitigation, and Explainable AI (XAI).
• Knowledge of Databases and SQL for data retrieval and preprocessing.
• Strong Analytical and Problem-Solving Skills to derive insights and optimize AI/ML solutions.

Preferred Qualifications:

• Bachelors or Master s degree in Data Science, AI, Machine Learning, or a related field.
• Experience with Generative AI, LLMs (Large Language Models), and Transformer Architectures.
• Familiarity with Kubernetes, Docker, and containerized ML deployments.
• Exposure to Graph Neural Networks, Time Series Forecasting, and Edge AI.
• Certification in AWS, Google, or Azure AI/ML platforms.