Machine Learning Developer

End to End Machine Learning Development

• Build and own ML solutions from data ingestion through modelling, evaluation, deployment, and monitoring.
• Develop, train, and evaluate machine learning models using modern ML frameworks and libraries.

Production Engineering & MLOps

• Deploy, operationalize, and maintain ML models in production environments, implementing CI/CD pipelines, Docker/containerization, orchestration, automated retraining, and monitoring.
• Write modular, production ready Python code and reusable ML components.

Data Preparation & Feature Engineering

• Extract, clean, transform, and validate datasets from diverse sources to support robust model development.
• Handle ambiguity in real world, imperfect data and design reproducible data processing pipelines.

Model Quality & Risk Management

• Apply rigorous evaluation practices: cross validation, bias/variance analysis, overfitting detection, and data leakage prevention.
• Monitor models for drift, performance degradation, and operational issues.

Collaboration & Stakeholder Engagement

• Work cross functionally with engineers, developers, architects, and project teams to align technical solutions with business objectives.
• Clearly communicate findings, risks, solution design, and technical trade offs to both technical and non technical stakeholders.

Innovation & Modern ML

• Work with emerging approaches such as LLMs, SLMs, embeddings, and prompt based workflows.
• Stay up to date with current ML engineering, MLOps practices, tooling, and cloud native capabilities.

• 5+ years of experience designing and implementing end to end ML solutions in production.
• Strong command of ML algorithms, model development, training, validation, and optimization.
• Expertise in Python, ML libraries, and version control (Git).
• Clear understanding of model evaluation, data leakage, and the bias/variance trade off.
• Hands on experience with cloud platforms (AWS/Azure/GCP) and MLOps practices, including Docker, CI/CD, deployment, and monitoring.
• Demonstrated success deploying and maintaining production ML models and writing modular, production grade code.
• Strong experience preparing, transforming, and validating complex real world datasets (in Snowflake or similar cloud data platforms).
• Experience with enterprise system data (SAP, Salesforce, PLM, Teamcenter) is desirable.
• Familiarity with LLMs/SLMs and modern ML frameworks (e.g., PyTorch, TensorFlow, HuggingFace).
• Excellent problem-solving abilities and communication skills.
• Proven ability to work cross-functionally with engineering and product teams.

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