Machine Learning Engineer

About The Role The role owns the end-to-end machine learning lifecycle — from data exploration and model development to deploying, monitoring, and iterating on models running in production. The team ships ML systems that handle high-volume, real-time inference where latency, accuracy, and cost efficiency are all first-class constraints. This is a hands-on engineering role, not a research-only position. The ML engineer will work closely with data engineers, backend teams, and product stakeholders to turn messy, real-world data into reliable ML-powered features that customers depend on daily. Key Responsibilities Design, train, and evaluate machine learning models — including gradient boosting, deep learning, and NLP models — for production use cases with measurable business impactBuild and maintain data pipelines and feature stores in Python, SQL, and Spark, ensuring consistency between offline training and online serving environmentsDeploy and serve models using Docker, Kubernetes, and cloud ML platforms (AWS, GCP, or Azure), including canary rollouts and rollback strategiesEstablish model monitoring and observability: tracking data drift, latency, and prediction quality with automated alerting and retraining triggersOptimize inference performance through model quantization, batching, caching, and hardware-aware deployment (GPU and CPU)Run rigorous experiments — A/B tests, offline evaluation, and error analysis — to validate model improvements before releaseContribute to ML platform tooling, documentation, and engineering standards; mentor junior engineers through code reviews What We Are Looking For 3–7 years of experience in machine learning engineering, applied ML, or a closely related role, with multiple models shipped to productionExpert-level Python and strong proficiency with PyTorch, TensorFlow, or scikit-learnProduction experience with ML deployment and serving stacks (Docker, Kubernetes, FastAPI, SageMaker, Vertex AI, or equivalent)Strong SQL skills and experience building feature/data pipelines with Spark, Airflow, or similar toolingSolid ML fundamentals: evaluation methodology, regularization, handling class imbalance, and the bias-variance tradeoff in practiceBachelor's degree in Computer Science, Engineering, Statistics, or a related quantitative field (or equivalent practical experience)Bonus: Experience with LLM/GenAI systems (RAG, fine-tuning, vector databases), MLOps tooling (MLflow, W&B, Kubeflow), or real-time streaming ML; Master's or PhD in a relevant field