Machine Learning Engineer

About The Role

The role involves architecting and deploying large-scale machine learning systems that power core product features, transitioning models from research environments to high-availability production APIs. This position focuses on the intersection of deep learning and scalable backend engineering, ensuring that models maintain peak performance while meeting strict latency requirements.

The engineer will collaborate with cross-functional teams to identify high-impact opportunities for ML intervention, specifically targeting complex problems in natural language processing and predictive analytics. The work contributes directly to a sophisticated tech stack where data-driven decisions and automated intelligence are central to the product strategy.

Key Responsibilities

• Develop and deploy production-grade ML models using PyTorch or TensorFlow, focusing on transformer-based architectures and deep neural networks.
• Build robust feature engineering and training pipelines using Spark, Ray, or Dask to handle multi-terabyte datasets across distributed environments.
• Implement and optimize inference infrastructure using NVIDIA Triton or TorchServe to reduce latency and improve throughput for real-time applications.
• Design automated MLOps workflows using Kubeflow or Airflow for continuous training, model versioning, and rigorous CI/CD for ML assets.
• Integrate monitoring solutions to track model health, detecting feature drift and performance decay through automated statistical validation.
• Collaborate on the design of internal ML platforms to standardize experimentation and accelerate the path from research to deployment.

What We Are Looking For

• 3–6 years of experience in machine learning engineering, with a proven track record of shipping models to production environments at scale.
• Expert-level proficiency in Python and solid experience with low-level languages like C++ or Go for performance-critical components.
• Deep technical knowledge of modern ML libraries: PyTorch, Hugging Face, scikit-learn, and XGBoost.
• Experience with containerization and orchestration using Docker and Kubernetes in a cloud environment (AWS, GCP, or Azure).
• Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related quantitative field.
• Bonus: Experience with LLM fine-tuning (PEFT/LoRA), quantization techniques, or contributing to open-source ML frameworks.