Machine Learning Engineer (Senior & Junior)

About the Role

We are looking for a Sr and Jr Machine Learning Engineer to design, develop, deploy, and continuously improve machine learning models that solve complex business problems using large-scale structured and unstructured data.

The ideal candidate will have strong experience building production-grade machine learning systems, developing predictive and behavioural models, performing advanced analytics, and collaborating with data engineering and software development teams to operationalize AI solutions.

This role requires a combination of machine learning expertise, software engineering skills, data science knowledge, and cloud deployment experience.

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.