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Niche MLOps specialization with mid-level experience in a metro, moderate employer brand drives medium competition.
Core MLOps and data engineering skills are transferable, but credit risk and model governance needs increase domain-specific fit.
Explicit 6+ years requirement plus mandatory MLOps, data engineering, and cloud tooling implies high shortlisting strictness.
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Design and build scalable data infrastructure, pipelines, and tools for automated post-deployment model monitoring adhering to governance policies.
Develop and maintain production-ready AI/ML pipelines including data ingestion, feature engineering, model training, and monitoring workflows with integration into MLOps platforms.
Collaborate cross-functionally with model developers, risk, compliance, and data teams to align monitoring strategies, ensure data lineage, and optimize performance for large-scale, real-time model monitoring.
Bachelor’s degree in a quantitative, technical, or data-focused field with 6+ years experience OR 8+ years relevant work experience in monitoring, validation, or credit risk strategy.
6+ years professional experience in model operations, data engineering, or analytics infrastructure.
Proficiency in data engineering tools (Apache Spark, Airflow, Kafka, dbt, PySpark) and programming languages (SAS, Python, SQL).
Experience with cloud data infrastructure (AWS, Azure, GCP) and data warehousing solutions (Snowflake, Redshift, BigQuery).
Experienced in operationalizing MLOps including model metadata tracking and monitoring toolkits, with strong understanding of model risk governance requirements.
A technical collaborator able to work in agile environments delivering production-grade code alongside DevOps and platform teams.
Skilled in building robust, high-performance data pipelines for real-time and batch processing supporting complex model monitoring scenarios.