ML Engineer

Role: ML Platform Engineer

Location: Pittsburgh, PA (Onsite)

Mode: Full Time

Job Summary 

Build and maintain scalable feature pipelines that power machine learning models. The role is primarily MLOps and feature engineering focused, with exposure to core data engineering concepts such as data ingestion and transformation. 

Key Responsibilities 

·         Design and implement feature pipelines (batch and real-time) 

·         Develop feature transformations and data processing logic 

·         Ensure feature quality, validation, and SLAs (freshness, accuracy, reliability) 

·         Work with upstream data pipelines and support data ingestion needs where required 

·         Monitor and optimize pipeline performance, latency, and cost 

·         Collaborate with Data Science and ML Engineering teams 

·         Support deployment, monitoring, and issue resolution 

·         Follow feature store and platform best practices 

Must-Have Skills 

·         Strong Python, SQL 

·         Experience with Spark / Flink or similar distributed processing 

·         Understanding of feature engineering and transformations 

·         Understanding of data pipelines and ETL concepts 

·         Exposure to cloud platforms (Azure / AWS / Google Cloud Platform). Experience with feature stores (Feast, Hopsworks, SageMaker) 

·         Knowledge of data quality and validation 

·         Familiarity with CI/CD and testing practices 

Good to Have 

·         Understanding of ML lifecycle 

·         Exposure to monitoring and observability tools 

·         Basic performance tuning experience 

Experience 

·         3–5 years in Feature Engineering, Data Engineering, or ML Engineering.