Machine Learning Engineer

Key Responsibilities

• Design, develop, train, validate, and optimize machine learning models.
• Build classification, regression, clustering, ranking, recommendation, anomaly detection, and forecasting models.
• Perform feature engineering, feature selection, model tuning, and model evaluation.
• Conduct experimentation and statistical analysis to improve model performance.
• Develop models that identify behavioural patterns, trends, anomalies, and relationships within large datasets.
• Develop predictive models using historical data.
• Build scoring frameworks and risk prediction models.
• Develop propensity, segmentation, and forecasting models.
• Develop graph-based analytics and relationship detection models.
• Perform hyperparameter tuning and model optimization.
• Compare model performance across different algorithms and approaches.
• Continuously improve accuracy, precision, recall, and explainability.
• Deploy machine learning models into production environments.
• Implement model monitoring, performance tracking, drift detection, retraining, and version control.
• Collaborate with engineering teams to integrate models into production systems and APIs.
• Work closely with Data Engineers to define data requirements and data quality standards.
• Collaborate with Software Engineers to operationalize machine learning solutions.
• Partner with business stakeholders to translate requirements into machine learning solutions.

Required Skills & Experience

• Strong experience designing, training, validating, and deploying machine learning models.
• Hands-on experience with classification, clustering, anomaly detection, predictive modelling, and scoring models.
• Strong understanding of feature engineering, model evaluation, and hyperparameter tuning.
• Advanced Python and Strong SQL and data analysis skills
• Experience with one or more of these frameworks- Scikit-Learn XGBoost / LightGBM / TensorFlow/ PyTorch
• Experience working with large datasets and building data-driven solutions.
• Familiarity with Spark / PySpark is preferred.
• Experience deploying machine learning solutions on AWS.
• Familiarity with S3, Lambda, SageMaker, and cloud-based ML workflows.
• Experience with Docker, CI/CD, model deployment, and model monitoring.

Ideal Candidate

• 5-10yrs+ years of hands-on experience developing and deploying machine learning models in production environments.
• Proven experience taking machine learning solutions from data exploration and model development through deployment, monitoring, and continuous improvement.
• Strong practical expertise in Python, SQL, and modern machine learning frameworks such as Scikit-Learn, XGBoost, TensorFlow, or PyTorch.
• Demonstrated experience working with large-scale datasets and solving complex analytical problems using machine learning techniques.
• Strong understanding of feature engineering, model evaluation, model explainability, and performance optimization.
• Experience collaborating with Data Engineering and Software Engineering teams to operationalize machine learning solutions.
• Comfortable working independently, conducting experiments, validating hypotheses, and translating ambiguous business problems into data-driven solutions.
• Strong software engineering discipline, including version control, testing, documentation, and production deployment practices.
• Experience with cloud-based machine learning environments, preferably AWS. Highly Preferred
• Experience in behavioural analytics, anomaly detection, predictive scoring, entity resolution, graph analytics, or network analysis.
• Experience building production-grade machine learning systems that directly influence business decisions or operational workflows.
• Experience working in highly regulated, data-intensive, or transaction-intensive environments.

Not Suitable For

• Candidates whose experience is primarily academic or research-based with limited production deployment experience.
• Candidates focused solely on reporting, dashboarding, or traditional business intelligence.
• Candidates with only GenAI/prompt engineering experience and limited machine learning fundamentals.
• Candidates without hands-on model development and deployment experience.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.