Work Location: Pittsburgh
Work Mode: Onsite
Pay Range: $100K-$107K /Yr Base + Annual Bonus
The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process
For more information on benefits and what we offer please visit us at US Careers and Benefits
US Careers and Benefits
The business outcomes we deliver to clients are made real by people through what they do at EXL. To be at the top of the game every day at work, requires you to bring your best.
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.
Responsibilities
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 / GCP). 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
Required Skills:
- 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
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