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Mid-level ML title, metro context, broad MLOps skillset, and popular candidate pool drive high competition.
Specialized ML platform, feature-store, and production MLOps expertise yield high background fit sensitivity.
Multiple mandatory MLOps, cloud, feature-store, and explicit years requirement indicate high shortlisting strictness.
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Own and improve core ML Platform systems including training pipelines, feature store, model deployment, and observability.
Build and operate production model training, batch and real-time inference, release, and monitoring systems.
Collaborate across teams such as Data Science, Risk, Product, Analytics, and Data Engineering to integrate ML infrastructure with business needs.
5–8+ years experience in backend, data infrastructure, or ML infrastructure with production system shipping experience.
Strong ML platform and MLOps expertise; hands-on with model training, inference, release, and monitoring production systems.
Proficiency in Python required; Go is a strong plus.
Strong AWS experience (SageMaker, S3, Lambda, Kinesis, DynamoDB), plus Kubernetes, Docker, and Terraform familiarity.
Experienced in building and operating production-scale feature stores ensuring point-in-time correctness and low-latency retrieval.
Comfortable managing data platform fundamentals including Snowflake, Airflow, streaming, ETL frameworks, and data quality.
Operates with a reliability/observability mindset focusing on SLOs, drift detection, explainability, and operational design.