Machine Learning Engineer

Role Overview

Design, implement, and scale production machine learning models that power core business logic. You’ll help move models from experimental notebook environments into high-throughput, low-latency production APIs, partnering with data platform and product engineering teams.

Key Responsibilities

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

Requirements

• 3–6 years of experience as an ML Engineer / Software Engineer (ML-focused) / Data Scientist in production.
• Strong software engineering in Python, including familiarity with:
  • asynchronous code
  • testing patterns
  • API frameworks such as FastAPI
• Hands-on experience with cloud ML infrastructure such as AWS SageMaker, GCP Vertex AI, or Azure ML.
• Familiarity with ML metadata/experiment tracking tools such as MLflow, Weights & Biases, or Kubeflow.
• BS/MS in a quantitative field (Computer Science, Data Science, Mathematics, etc.).

Nice to Have

• Experience with LLM fine-tuning
• RAG (Retrieval-Augmented Generation)
• Vector databases such as Pinecone