Machine Learning Engineer

Responsibilities

• check_circle Design, build, and deploy machine learning models for client use cases
• check_circle Develop end-to-end ML pipelines: data preparation → feature engineering → training → validation → deployment
• check_circle Collaborate with data engineers to ensure reliable data pipelines and high-quality features
• check_circle Implement MLOps practices, including model versioning, monitoring, CI/CD, and lifecycle management
• check_circle Build predictive analytics solutions such as classification, regression, forecasting, and anomaly detection
• check_circle Translate business requirements into scalable ML solutions in collaboration with client teams
• check_circle Evaluate models objectively and optimize performance using appropriate metrics
• check_circle Deploy and operate models on Azure, AWS, or client-specific cloud environments
• check_circle Document solutions and follow best engineering and ML best practices
• check_circle Stay up to date with emerging ML/AI tools, frameworks, and techniques

Basic qualifications

• Bachelor’s degree in Computer Science, Data Science, Machine Learning, Engineering, or a related field
• Strong Python programming skills
• Hands-on experience with ML frameworks such as:
• Scikit-learn, XGBoost, LightGBM
• Bonus: TensorFlow or PyTorch
• Experience developing ML models for real-world or production-oriented problems
• Solid understanding of feature engineering, model evaluation, and validation techniques
• Experience with cloud-based ML platforms such as Azure ML, AWS SageMaker, or similar
• Understanding of the ML lifecycle, reproducibility, and model monitoring
• Familiarity with Docker and APIs for model deployment
• Strong analytical thinking, problem-solving, and communication skills
• Ability to work independently while collaborating with cross-functional teams
• Experience with MLOps tools such as MLflow, DVC, Feast, Databricks
• Experience with deep learning frameworks (PyTorch, TensorFlow)
• Experience with time-series forecasting
• Familiarity with Spark or distributed data processing
• Experience integrating ML models into client applications (microservices, APIs)
• Knowledge of DevOps practices (CI/CD, GitHub Actions, Azure DevOps, etc.)

About the company