AI/ML Engineer

The AI/ML Engineer will design, develop, and validate machine learning models that support product features and internal analytics. Working closely with data engineers and product owners, the engineer translates business requirements into data pipelines, feature sets, and model prototypes. The role owns end-to-end model development for assigned use cases, including data preparation, algorithm selection, training, evaluation, and documentation. By monitoring model performance in production, the engineer helps identify drift and suggests improvements. This position requires independent execution of defined projects, clear communication of technical findings, and collaboration with cross‑functional teams to ensure models meet accuracy and reliability standards.
Key Responsibilities

• Gather and clean structured and unstructured data sets required for model training, documenting data lineage and quality checks.
• Develop feature engineering pipelines using Python and pandas to create reproducible inputs for machine learning algorithms.
• Select, implement, and fine‑tune supervised learning models (e.g., regression, classification) with scikit-learn or TensorFlow, adhering to project constraints.
• Conduct systematic model evaluation, generating performance reports and visualizations for stakeholder review.
• Write reusable code modules and unit tests, maintaining version control with Git and following team coding standards.
• Deploy trained models to a staging environment using Docker containers and validate inference latency and resource usage.
• Monitor deployed models for performance decay, collect feedback, and propose retraining or parameter adjustments.
• Collaborate with product managers to translate business objectives into measurable model success criteria.
• Document model architecture, training procedures, and operational guidelines in the teams knowledge base.
  

Required Qualifications

• Bachelors degree in Computer Science, Electrical Engineering, Statistics, or a related quantitative field.
• 1 to 2 years of professional experience building and deploying machine learning models.
• Proficiency in Python programming, including libraries such as pandas, NumPy, and scikit-learn.
• Experience with at least one deep learning framework (TensorFlow or PyTorch).
• Demonstrated ability to write clear, maintainable code and use Git for version control.
• Strong analytical skills with the ability to interpret model results and communicate findings to non‑technical stakeholders.
  

Required Skills

• Data preprocessing and feature engineering
• Model selection, training, and hyperparameter tuning
• Performance evaluation metrics (accuracy, precision, recall, AUC)
• Version control with Git
• Containerization basics using Docker

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  Clear technical documentation and reporting
  

Preferred Qualifications

• Masters degree in a quantitative discipline.
• Experience with cloud services for data storage or model hosting (e.g., AWS S3, Azure Blob).
• Exposure to MLOps tools such as MLflow or Kubeflow.
• Published project or paper demonstrating end‑to‑end machine learning workflow.
  

Preferred Skills

• Time‑series forecasting techniques
• Natural language processing using spaCy or Hugging Face Transformers
• Automated testing of model pipelines
• Knowledge of SQL for data extraction