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 impact
• Build and maintain data pipelines and feature stores in Python, SQL, and Spark, ensuring consistency between offline training and online serving environments
• Deploy and serve models using Docker, Kubernetes, and cloud ML platforms (AWS, GCP, or Azure), including canary rollouts and rollback strategies
• Establish model monitoring and observability: tracking data drift, latency, and prediction quality with automated alerting and retraining triggers
• Optimize 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 release
• Contribute 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 production
• Expert-level Python and strong proficiency with PyTorch, TensorFlow, or scikit-learn
• Production 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 tooling
• Solid ML fundamentals: evaluation methodology, regularization, handling class imbalance, and the bias-variance tradeoff in practice
• Bachelor'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