About The Role

The Machine Learning Engineer will design, build, and deploy production ML systems across areas such as recommendation, ranking, forecasting, classification, and natural language processing. The role spans experimentation, feature engineering, model serving, and operational ownership in environments where accuracy, latency, scalability, and reliability all matter.

Working with data scientists, software engineers, and MLOps partners, the role will turn research prototypes into maintainable services used by real customers. The team is building a robust model platform with reproducible training pipelines, automated evaluation, real-time inference, and monitoring for drift and performance regression.

Key Responsibilities

Design, train, and evaluate machine learning models using Python, PyTorch, TensorFlow, or scikit-learn for production use cases
Build scalable feature engineering and data pipelines with SQL, Spark, Airflow, or equivalent workflow orchestration tools
Deploy and operate batch and real-time inference services using Docker, Kubernetes, and cloud infrastructure such as AWS SageMaker, EKS, or Vertex AI
Develop model serving APIs and optimize inference performance for throughput, latency, cost, and resource utilization
Implement experiment tracking, dataset and model versioning, reproducible training workflows, and automated validation gates
Monitor production models for data drift, feature quality, prediction distribution changes, and business metric degradation using automated alerting
Collaborate in architecture reviews and code reviews while establishing testing, documentation, and ML engineering standards across the team

What We Are Looking For

3–8 years of experience in machine learning engineering, applied machine learning, or a closely related production engineering role
Strong Python skills with experience building reliable, testable services and data processing workflows
Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn, including model evaluation and hyperparameter tuning
Experience deploying and maintaining machine learning models in production using cloud platforms, containers, Kubernetes, or managed ML services
Solid understanding of supervised learning, feature engineering, model validation, statistical evaluation, and common failure modes such as leakage and distribution shift
Bachelor’s or master’s degree in computer science, machine learning, statistics, mathematics, engineering, or a related technical field
Bonus: Experience with LLMs, recommender systems, NLP, feature stores, MLflow, Kubeflow, Spark, distributed training, or model observability platforms

Job description