Machine Learning Engineer

Role Overview

Design, implement, and scale machine learning systems from training to real-time inference. You’ll build production-grade ML architectures that translate business requirements into performant models and reliable ML operations.

Responsibilities

• Build end-to-end ML pipelines (data ingestion → feature engineering → training → deployment)
• Deploy deep learning and classical ML models to high-throughput production environments using Kubernetes, Triton Inference Server, or TorchServe
• Create and maintain scalable feature stores and data pipelines using PySpark, dbt, and Snowflake to ensure training/serving data consistency
• Implement MLOps practices:
  • automated model monitoring and drift detection
  • ML CI/CD pipelines
  • model/version tracking with MLflow or Weights & Biases
• Optimize model performance and inference latency (e.g., quantization, pruning, efficient tensor operations)
• Partner with product managers and backend engineers to integrate model outputs and define KPIs

Requirements

• 3–6 years experience as an ML Engineer, ML-focused Software Engineer, or Applied Scientist, with proven production model deployment
• Strong Python skills and solid software engineering fundamentals (OOP design, unit testing, Git)
• Deep ML framework expertise: PyTorch, TensorFlow, or JAX
• Data science library proficiency: scikit-learn, Pandas, NumPy
• Hands-on cloud and infrastructure experience (AWS or GCP) and containerization (Docker, Kubernetes)
• Understanding of statistical modeling, ML algorithms, and performance evaluation metrics

Bonus

• Experience with LLMs and techniques like prompt engineering and fine-tuning (LoRA, QLoRA)
• Managing vector databases (e.g., Pinecone, Milvus, Qdrant)