Machine Learning Engineer

Role Overview

Design, implement, and scale machine learning models that power core product features and real-time decision engines. You will build production-ready data pipelines and deploy ML systems with sub-millisecond latency and high reliability, working closely with data scientists and platform/backend engineers.

Responsibilities

• End-to-end ML development: design, train, and optimize ML models for tabular and NLP use cases.
• Production deployment: deploy ML pipelines and APIs on AWS using SageMaker, Kubernetes, and Docker.
• Data & feature engineering: build and maintain scalable feature stores and data pipelines using PySpark, SQL, and dbt.
• Monitoring & reliability: implement automated monitoring/alerting for model drift, concept drift, and performance degradation.
• Systems integration: collaborate with backend engineers to integrate model endpoints into a microservices architecture for low-latency inference.
• MLOps best practices: establish CI/CD for ML, model versioning with MLflow or DVC, and automated retraining.

Requirements

• 3–6 years of experience as a Machine Learning Engineer or Software Engineer with a strong focus on production ML systems.
• Strong Python proficiency and solid software engineering fundamentals (e.g., unit testing, clean code practices, system design).
• Hands-on experience with cloud infrastructure (AWS or GCP) and containerization (Docker, Kubernetes).
• Deep understanding of ML algorithms, evaluation/optimization methods, and statistical modeling.
• BS/MS/PhD in CS, Data Science, Mathematics, or a related quantitative field.

Bonus / Nice to Have

• Real-time streaming technologies such as Kafka.
• Experience building GenAI/LLM pipelines with LangChain.