Machine Learning Engineer

About The Role

The Machine Learning Engineer designs, builds, and deploys ML systems that run in production — not demos. This role owns model development from data pipeline through serving, working with PyTorch, Python, and modern MLOps tooling on cloud infrastructure. The team ships models that handle real traffic, real latency budgets, and real business impact.

This role matters because reliable ML in production is what separates companies that talk about AI from companies that make money with it.

Key Responsibilities

Design, train, and evaluate ML models (classification, ranking, forecasting, NLP) for production use cases with strict accuracy and latency requirements Build and maintain data and feature pipelines using Python, PySpark, and tools like Airflow or dbt, ensuring consistency between training and serving Deploy and serve models using Docker, Kubernetes, and cloud ML platforms (AWS SageMaker, GCP Vertex AI, or Azure ML), including A/B testing and rollback strategies Instrument models in production for drift detection, performance monitoring, and automated alerting; own incident response for model quality issues Optimize inference performance — batching, quantization, caching, and model distillation — to meet cost and latency targets Collaborate with data engineers, product managers, and backend teams to scope problems, define success metrics, and ship end-to-end ML features Contribute to team standards: code reviews, experiment tracking (MLflow, W&B), reproducible training pipelines, and documented model cards What We Are Looking For 3–6 years of experience in machine learning engineering, applied ML, or closely related roles, with multiple production deployments Expert-level Python and hands-on depth in at least one major framework: PyTorch, TensorFlow, or scikit-learn Proven experience deploying and operating models in production on AWS, GCP, or Azure, including CI/CD for ML systems Strong ML fundamentals: feature engineering, evaluation metrics, regularization, and diagnosing training/serving skew Experience with orchestration and infrastructure tooling: Docker, Kubernetes, Airflow, Terraform, or equivalents Bachelor's degree in Computer Science, Engineering, Statistics, or a related quantitative field (or equivalent practical experience) Bonus: LLM/GenAI experience (RAG, fine-tuning, inference optimization), experience with Spark at scale, a published open-source contribution, or a relevant MS/PhD