Requirements

5+ years of engineering experience in the Data and/or AI/ML space
Strong software engineering background, with proficiency in Python (familiarity with Rust is a plus)
Proven experience designing and implementing large-scale batch data processing workflows (e.g., Pandas, Spark, Polars, SQL)
Hands-on experience developing and operating real-time, low-latency, streaming data pipelines (e.g., Apache Flink, Kafka, Beam)
Proven experience in building and maintaining AI/ML or data platforms at scale.
Solid understanding of MLOps concepts — including model lifecycle management, CI/CD for ML, feature stores, and data validation frameworks.
Experience with GenAI tools, such as Langchain, LlamaIndex, and open source Vector DBs.
Hands-on experience with containerization and orchestration technologies such as Docker, Kubernetes, or Nomad.
A pragmatic mindset, able to balance technical rigor, performance, and delivery timelines.
Excellent communication and collaboration skills, with the ability to partner across disciplines.
A passion for building secure, reliable systems that support mission-critical business functions.

Nice to Haves

Design and implement robust Python services and libraries to power real-time fraud detection and compliance monitoring systems.
Build scalable batch and streaming data pipelines for feature building, model inference, and training, with strong guarantees for data quality, latency, and reliability.
Develop and operate a developer-friendly ML platform, including feature stores, data validation tooling, and model-training pipelines with automated quality checks.
Support ML model lifecycle management — from feature engineering and experimentation to deployment, monitoring, and performance tuning.
Manage and optimize real-time data infrastructure, leveraging technologies such as Kafka, Apache Flink, and Kubernetes/Nomad.
Work closely with experienced Python and Rust engineers, data scientists, and business stakeholders.
Mentor junior engineers, foster best practices, and help shape a strong engineering culture.
Stay current with MLOps, AI infrastructure, and data engineering trends, bringing innovative solutions into production.
Take end-to-end ownership of systems and services, ensuring scalability, maintainability, and security.