Responsibilities:

1. Build and own scalable ML systems covering feature pipelines, training, inference, and monitoring.

2. Develop and productionize real-time and batch ML pipelines with high reliability and performance.

3. Design and implement reusable, production-grade ML infrastructure and tooling.

4. Collaborate with Data Scientists to productionize models and improve system performance.

5. Build MVPs to demonstrate business impact and evolve them into production systems.

6. Participate in system design discussions and contribute to architecture decisions.

7. Ensure best practices in code quality, testing, and deployment.

8. Work with Python, Spark, AWS, Snowflake, and modern ML tooling.

Requirements

1. 2–5 years of experience in Machine Learning Engineering, or backend/data engineering with ML exposure.

2. Strong understanding of ML systems: training pipelines, inference, feature engineering, model monitoring, and retraining.

3. Experience building and deploying ML models in production environments.

4. Proficiency in Python and familiarity with building APIs (FastAPI/Flask).

5. Experience with distributed data processing (ex Spark) and cloud platforms (AWS/GCP/Azure).

6. Familiarity with ML frameworks like PyTorch, TensorFlow, or Keras.

7. Understanding of CI/CD, experimentation, and model lifecycle management is a plus.

8. Solid foundation in statistics, linear algebra, and ML concepts.

9. Strong problem-solving and communication skills.

[3:26 PM]

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