Machine Learning Engineer

Role Overview

Own the end-to-end lifecycle of machine learning systems, bridging exploratory ML work with production-ready architecture. You will build scalable ML pipelines, optimize inference latency, and deploy robust predictive models into a microservices environment.

Responsibilities

• Design and implement scalable ML pipelines in Python using PyTorch, scikit-learn, and Hugging Face.
• Deploy, monitor, and scale ML models in production using AWS SageMaker, Kubernetes, and Docker.
• Build low-latency inference endpoints and integrate them with existing microservices.
• Collaborate with data engineers to design feature stores and ensure data integrity across training and inference.
• Monitor model performance for data drift, concept drift, and degradation using automated logging/monitoring.
• Write tests, maintain clean documentation, and participate in code reviews to uphold engineering standards.

Requirements

• 3–6 years of professional software engineering experience, with at least 3 years in ML engineering.
• Strong proficiency in Python, SQL, and distributed computing frameworks such as Spark or Ray.
• Hands-on experience deploying and managing ML models in cloud environments (AWS, GCP, or Azure).
• Solid understanding of software design principles, CI/CD, and Infrastructure-as-Code (e.g., Terraform).
• BS or MS in Computer Science, Statistics, Mathematics, or related field.

Bonus

• Experience with LLM fine-tuning.
• Experience with RAG (Retrieval-Augmented Generation) architectures.
• Contributions to open-source ML projects.