Machine Learning Engineer

About The Role

The role drives the development and deployment of production-grade machine learning models, bridging the gap between theoretical data science and highly scalable software engineering. The team focuses on deploying real-time prediction services that operate under strict latency and throughput constraints. This position collaborates closely with data engineers and product managers to build robust, reproducible ML pipelines. The work directly impacts core product features, requiring a strong grasp of both algorithmic concepts and modern MLOps practices.

Key Responsibilities

• Design, train, and optimize machine learning models for classification, recommendation, and NLP tasks using PyTorch and scikit-learn
• Deploy models to production environments using Docker and Kubernetes, utilizing Triton Inference Server or FastAPI for high-throughput serving
• Develop and maintain automated machine learning pipelines (orchestrated via Kubeflow or Airflow) for feature extraction, model training, and continuous evaluation
• Implement model monitoring systems using Prometheus and Grafana to track data drift, prediction latency, and system resource utilization
• Optimize model inference performance through techniques such as quantization, pruning, and ONNX runtime integration
• Write clean, modular, and thoroughly tested Python code within a collaborative Git-based workflow, participating in regular code and architecture reviews

What We Are Looking For

• 3–6 years of professional experience as a Machine Learning Engineer or Software Engineer working on production ML systems
• Strong proficiency in Python and solid experience with ML frameworks such as PyTorch, TensorFlow, or XGBoost
• Hands-on experience with cloud infrastructure (AWS or GCP) and containerization tools like Docker and Kubernetes
• Solid understanding of software engineering best practices, including CI/CD, unit testing, and design patterns
• Bachelor's or Master's degree in Computer Science, Data Science, or a related quantitative field
• Bonus: Experience with vector databases, MLflow, feature stores (Feast), or distributed data processing (PySpark)