Lead design and implementation of scalable batch and real-time feature pipelines, own MLOps practices, contribute to feature store and platform architecture, optimize performance and cost, ensure feature quality and monitoring, deploy and resolve production incidents, and mentor junior engineers.
Work Location: Pittsburgh
Work Mode : Onsite
Pay Range :$110K-$125K /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 https://www.exlservice.com/us-careers-and-benefits
Job Summary:
Lead the design and development of scalable feature platforms and ML pipelines. Own MLOps practices, contribute to platform architecture, and mentor engineers while incorporating key data engineering best practices. The person is expected to do hands on work as well.
Responsibilities
Key Responsibilities:
- Design and implement scalable, reusable feature pipelines (batch and real-time)
- Develop advanced feature transformations and complex data models
- Optimize performance, latency, and cost efficiency
- Ensure feature quality, validation, and SLAs
- Work closely with data engineering teams on upstream data pipelines and ingestion design
- Contribute to feature store and data platform architecture
- Collaborate across Data Science, MLOps, and Platform teams
- Lead production deployment, monitoring, and incident resolution
- Mentor junior engineers and drive engineering best practices
- Translate business use cases into scalable feature logic
Must-Have Skills:
- Advanced Python and SQL
- Strong experience with distributed processing (Spark / Flink)
- Deep expertise in feature engineering patterns
- Strong understanding of data pipelines and ETL architecture
- Experience with feature stores
- Strong understanding of ML lifecycle and model optimization
- Experience with CI/CD, monitoring, and production systems
- Cloud platform experience (Azure / AWS / GCP)
- Experience in data quality, validation, and drift detection
Good to Have:
- Experience building enterprise feature or ML platforms
- Advanced performance tuning and cost optimization
- Exposure to broader data platform architecture
- Strong stakeholder communication and leadership experience
Experience:
- 6–10+ years with mentoring/technical leadership experience
Qualifications
Required skills:
- Advanced Python and SQL
- Strong experience with distributed processing (Spark / Flink)
- Deep expertise in feature engineering patterns
- Strong understanding of data pipelines and ETL architecture
- Experience with feature stores
- Strong understanding of ML lifecycle and model optimization
- Experience with CI/CD, monitoring, and production systems
- Cloud platform experience (Azure / AWS / GCP)
- Experience in data quality, validation, and drift detection
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