Support R&D of distributed, highly scalable, and fault-tolerant microservices
Use test-driven development techniques to develop beautiful, efficient, and secure code
Create and scale high-performance services that bring new capabilities to Arctic Wolf s data science organizations
Execute on deliverables on the roadmap of ML engineering, modeling, and operations at Arctic Wolf
Identify problems proactively and propose novel solutions to solve them
Continuously learn and expand your technical horizons
WE RE LOOKING FOR SOMEONE WHO:
Will collaborate closely with our data science and ML teams across different cybersecurity domains to define ML infrastructure requirements and build critical data services
Can leverage ML Ops best practices to design and develop scalable model training, evaluation, experimentation and deployment workflows
Has extensive experience in ML training (local and distributed), feature extraction, dataset creation
Is comfortable deploying software with CI / CD tools including Jenkins, Harness, Terraform etc.
Is an expert at developing and deploying assets in the cloud - preferably AWS and Kubernetes using IAC (infrastructure as code)
Can build a workflow orchestration platform to be used by other developers
Has hands-on experience implementing data pipeline infrastructure for data ingestion and transformation near real-time availability of data for applications and ML pipelines
Has experience designing optimized solutions for ingestion, curation of large datasets
Has working knowledge of Data Lake technologies, data storage formats (Parquet, ORC, Avro), and query engines (Athena, Presto, Dremio ) and associated concepts for building optimized solutions at scale
Maintains a proficient level in one of the following programming languages or similar- Python, Java, Go
Has experience with data pipelines tools (Flink, Spark or Ray) and orchestration tools such as Airflow, Dagster or Step Functions
Is an expert in implementing data streaming and event-based data solutions (Kafka, Kinesis, SQS/SNS or the like)
