Machine Learning Engineer

About The Role

The role focuses on building, scaling, and maintaining the core machine learning infrastructure and production models that power real-time personalization and prediction engines. This engineer will bridge the gap between theoretical data science and robust systems engineering, translating complex algorithms into reliable, low-latency APIs serving millions of requests daily.

Working within a high-caliber technical team, this individual will own the engineering lifecycle of predictive models. The work directly impacts core business metrics by improving model accuracy, reducing training and inference latency, and ensuring continuous deployment capabilities.

Key Responsibilities

• Develop and deploy production-grade machine learning models for ranking, classification, and recommendation systems using PyTorch and scikit-learn
• Design and implement scalable feature engineering pipelines using Spark, SQL, and internal feature stores to support both offline training and real-time serving
• Build robust inference APIs and integrate them into microservice architectures with Docker, Kubernetes, and FastAPI
• Establish automated MLOps pipelines for continuous integration, model evaluation, deployment, and monitoring using MLflow and AWS SageMaker
• Optimize model serving performance, including model quantization, pruning, and GPU/CPU resource utilization management
• Collaborate with data scientists and backend engineers to define interfaces, establish evaluation metrics, and run systematic A/B tests in production

What We Are Looking For

• 3-6 years of experience as a Machine Learning Engineer or Software Engineer deploying production ML systems at scale
• Strong proficiency in Python and solid software engineering practices, including version control, CI/CD pipelines, and unit testing
• Hands-on experience with core ML frameworks such as PyTorch, TensorFlow, or XGBoost, and data processing tools like Pandas, SQL, and Spark
• Familiarity with cloud-based ML infrastructure (AWS SageMaker or GCP Vertex AI) and containerization tools like Docker and Kubernetes
• BS or MS in Computer Science, Data Science, Mathematics, or a related quantitative field
• Bonus: Experience with large language models (LLMs), retrieval-augmented generation (RAG) pipelines, or vector databases such as Pinecone and Milvus