SummaryThe role of Applied Data Scientist – MLOps focuses on developing and deploying data science solutions while ensuring smooth operational functionality across business areas. The primary objective is to build scalable and stable MLOps pipelines for robust model deployment and monitoring, contributing to clients enhanced use of ML and AI.Main Responsibilities:Design and develop data science solutions using traditional ML and modern modeling techniques.Perform exploratory data analysis (EDA), feature engineering, and data preprocessing for model development.Construct, test, and validate supervised and unsupervised ML models, optimizing algorithms and hyperparameters for robustness.Lead deployment of ML/AI models into production using CI/CD and containerized workflows.Develop reproducible ML pipelines for various deployment processes such as training, testing, and serving.Deploy LLM-powered applications and build scalable back-end infrastructure on platforms like Azure OpenAI and Hugging Face.Develop automation scripts to optimize data pipelines and deployment workflows.Collaborate with IT and engineering teams to ensure successful model integration into existing systems.Key Requirements:Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.3-4 years of hands-on experience as a Data Scientist or ML Engineer focused on model deployment.Strong Python programming skills (Pandas, NumPy, Scikit-learn).Proficiency in ML frameworks: TensorFlow, PyTorch, MLflow, Hugging Face.Practical experience with MLOps workflows and CI/CD (e.g., GitHub Actions, Azure DevOps).Nice to Have:Master’s degree or certifications in ML/AI/MLOps.Experience with LLMs and RAG pipelines.Deep understanding of MLOps tooling such as Airflow and Kubernetes.Ability to build APIs using FastAPI or Flask.Other Details:Job Context: Supports clients use of ML and AI with a focus on stable MLOps pipelines.Team Collaboration: Works closely with IT, engineering, and business teams.