Early work centers on supporting model development pipelines. Tasks include cleaning and preprocessing datasets with Python (Pandas, NumPy), implementing basic algorithms (regression, classification), and running experiments under close supervision. Junior developers also write unit tests, document workflows, and assist in preparing training data using tools like Jupyter Notebooks. Exposure to machine learning libraries (scikit-learn, TensorFlow, PyTorch) and version control (Git) is emphasized, along with learning how to use ML experiment tracking tools such as MLflow or Weights & Biases.

Semi-senior developers independently build and optimize models for defined use cases, such as recommendation engines, fraud detection, or sentiment analysis. They refine hyperparameters, evaluate models using metrics like precision, recall, or F1-score, and optimize data pipelines with tools such as Airflow or Luigi. Responsibilities also include packaging models into APIs for integration, deploying them to cloud platforms (AWS SageMaker, Azure ML, or GCP Vertex AI), and collaborating with data engineers to ensure production-ready systems. At this stage, developers are expected to handle end-to-end model delivery for medium-complexity projects.

Senior developers lead the design and deployment of advanced machine learning solutions, often using deep learning architectures, natural language processing (NLP), or computer vision models. They guide architectural decisions, balance experimentation with production stability, and oversee performance monitoring at scale. Tasks include building reusable ML components, applying MLOps practices (CI/CD pipelines for models), and supervising data governance and ethical AI practices. Senior developers mentor junior colleagues, conduct technical reviews, and evaluate new frameworks or techniques to keep the team at the cutting edge.

Strategy and leadership define this level. The Machine Learning Development Manager oversees teams of developers, sets standards for MLOps practices, and aligns machine learning projects with organizational priorities. Responsibilities include budgeting for compute resources, evaluating cloud and infrastructure partners, and ensuring compliance with regulations around data privacy and AI ethics. The role involves close collaboration with executives and product managers to translate business problems into ML-driven solutions, while promoting innovation through advanced technologies such as reinforcement learning, generative AI, or federated learning.