Machine Learning Engineer

About The Role

The role is responsible for the end-to-end design, implementation, and scaling of machine learning models that drive core business logic. The team focuses on moving models quickly from experimental notebook environments into high-throughput, low-latency production APIs.

This position collaborates closely with data platform teams and product engineers to build robust training, inference, and evaluation systems. The primary objective is to maintain high model reliability and accuracy while managing computational efficiency.

Key Responsibilities

• Design and deploy production-grade ML models utilizing PyTorch or TensorFlow for real-time inference and batch scoring systems
• Build and maintain feature stores and data preparation pipelines using PySpark, SQL, and pandas across cloud environments
• Implement automated CI/CD pipelines for ML, including containerization with Docker and deployment orchestration via Kubernetes
• Establish model monitoring pipelines to track feature drift, performance degradation, and system latency with Prometheus and Grafana
• Collaborate on model evaluation frameworks to run automated regression testing and online A/B tests before full production rollouts

What We Are Looking For

• 3-6 years of experience as a Machine Learning Engineer, Software Engineer (ML focus), or Data Scientist in a production environment
• Strong software engineering foundation in Python, including familiarity with writing asynchronous code, testing patterns, and API frameworks like FastAPI
• Hands-on experience with cloud-based ML infrastructure such as AWS SageMaker, GCP Vertex AI, or Azure ML
• Familiarity with ML metadata and tracking tools like MLflow, Weights & Biases, or Kubeflow
• BS or MS in Computer Science, Data Science, Mathematics, or a highly quantitative field
• Bonus: Experience with large language model (LLM) fine-tuning, retrieval-augmented generation (RAG), or vector databases like Pinecone