Data Scientist-Machine Learning Engineer

Role & responsibilities

• Proficiency in Python, TensorFlow, PyTorch, and basic ML/DL concepts
• Experience in containerization (Docker, Kubernetes) and cloud-based deployments
• Familiarity with NLP, embeddings, and transformer models
• Strong software engineering skills, CI/CD pipelines, and API integration (Flask, FastAPI)
• Understanding of model evaluation and performance tuning
• Exposure to MLOps tools (MLflow, Kubeflow) and model observability (Prometheus, Grafana)

Preferred candidate profile

• Assist in building and deploying ML models with best practices
• Containerize and ship ML models for production use
• Compare and evaluate model performance, ensuring optimal results
• Support model fine-tuning and dataset preparation
• Collaborate on ML workflow automation and monitoring
• Document processes and stay updated with ML advancements