Machine Learning Engineer

About The Role

The role owns the full lifecycle of machine learning systems, from experimental prototyping to robust, production-grade deployment serving scalable workloads.

The team works collaboratively across data science and backend engineering to solve complex infrastructure problems where model latency, throughput, and accuracy are equally critical.

Key Responsibilities

• Design, train, and evaluate machine learning models for production use cases involving NLP, recommendation systems, and predictive analytics
• Build and maintain robust feature engineering and ETL pipelines using Python, SQL, and PySpark to ensure data integrity across training and serving
• Deploy, scale, and manage ML workloads using Docker, Kubernetes, and cloud ML platforms like AWS SageMaker or GCP Vertex AI
• Monitor production models for data drift, concept drift, and latency degradation using automated logging and alerting frameworks
• Optimize model inference performance through quantization, pruning, and hardware acceleration techniques
• Write clean, testable, and well-documented code while actively participating in technical architecture and code reviews
  
  

What We Are Looking For

• 3–6 years of professional software engineering or machine learning engineering experience, with at least two years deploying models to production
• Strong proficiency in Python and hands-on experience with deep learning frameworks such as PyTorch or TensorFlow
• Solid understanding of containerization and orchestration tools including Docker and Kubernetes
• Experience with cloud infrastructure and MLOps tooling (AWS, GCP, MLflow, or Kubeflow)
• BS or MS in Computer Science, Machine Learning, Statistics, or a related technical field
• Bonus: Experience fine-tuning large language models or building production RAG systems