Senior MLOps Engineer

Candidates should possess strong general engineering skills, primarily in Python, with versatility across data engineering including data stores like ClickHouse, ElasticSearch, Postgres, Redis, and Neo4j. Experience with data pipeline and workflow orchestration tools such as Airflow, DBT, Luigi, Azkaban, and Storm is required, alongside proficiency in data processing and streaming workflows using Spark, Kafka, and Flink. Expertise in deployment and monitoring infrastructure on public cloud platforms including Docker, Terraform, Kubernetes, and Datadog is essential, as is experience with AI and ML integration tools like MLflow, Feast, ONNX, and Apache Beam. The ideal candidate is comfortable working with noisy, dirty, and unstructured data, demonstrates high initiative and ownership, excels at problem structuring and prioritization, and can communicate complex ideas effectively to diverse audiences. Prior experience in a cross-functional environment and a passion for building a safer financial system are also key requirements. Interest in virtual currencies and blockchain analysis is a plus.