Machine Learning Engineer

The Machine Learning Engineer is responsible for designing, building, deploying, and maintaining scalable machine learning models and systems that power data-driven products and services. This role bridges data science and software engineering, focusing on production-grade ML solutions.

Key Responsibilities

Model Development & Deployment

• Design, develop, and train machine learning models for real-world applications
• Deploy models into production environments with scalability and reliability in mind
• Optimize model performance, accuracy, and efficiency

Data Engineering & Pipelines

• Build and maintain data pipelines for training, validation, and inference
• Ensure data quality, consistency, and availability
• Collaborate with data engineers and analysts on data infrastructure

MLOps & Monitoring

• Implement MLOps practices including CI/CD for ML, model versioning, and monitoring
• Track model performance, drift, and retraining needs
• Automate model deployment, testing, and rollback processes

Collaboration & Product Integration

• Work closely with product managers, data scientists, and software engineers
• Translate business requirements into ML-driven solutions
• Support experimentation, A/B testing, and feature iteration

Required Qualifications

Education

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field

Experience

• 3–6 years of experience in machine learning, data science, or software engineering
• Experience deploying ML models in production environments

Technical Skills

• Strong programming skills in Python (and/or Java, Scala)
• Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Solid understanding of ML algorithms, statistics, and data structures
• Experience with data processing tools (Pandas, NumPy, Spark)
• Familiarity with APIs and microservices for model serving

Cloud & DevOps

• Experience with cloud platforms (AWS, Azure, Google Cloud Platform)
• Familiarity with containerization and orchestration (Docker, Kubernetes)
• Knowledge of CI/CD pipelines and Infrastructure as Code is a plus

Preferred Qualifications

• Experience with large-scale or real-time ML systems
• Knowledge of MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI)
• Experience with NLP, computer vision, or recommendation systems
• Familiarity with SQL and NoSQL databases
• AI/ML certifications are a plus