Lead ML Developer - R&D

Responsibilities :

- 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.

- Influence the work of team members and mentor emerging technical leaders.

- Develop trusted cross-team relationships to deliver solutions that span multiple areas of expertise.

- Identify problems proactively and propose novel solutions to solve them.

- Continuously learn and expand your technical horizons.

Requirements :

- 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 MLOps 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, and 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 of 2+ years 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 pipeline 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).