Role Description As a Senior Software Engineer, you will play a key role in developing and maintaining the core infrastructure that underpins our AI platform. Your expertise in coding and architecture will be instrumental in building out core platform features, while your proficiency in Rust will enable you to contribute to the growth of our technology stack. Your experience with production support, Kubernetes, and Docker will ensure the platform's stability and reliability. - Design and develop foundational components and frameworks that serve as the building blocks for our Agentic AI platform. - Collaborate with cross-functional teams to understand their needs and requirements, and create platform solutions that empower developers to build innovative applications on top of it. - Cultivate a growth mindset, constantly studying and staying current with design patterns, techniques, and technologies that attract developers to platforms and frameworks. - Work closely with machine learning engineers to build the infrastructure that supports ML workflows, ensuring seamless and efficient execution within enterprises. - Provide production support and troubleshooting expertise to ensure the platform's smooth operation and address any performance or scalability challenges. Qualifications - US Citizenship or Greencard is required due to US Government contract requirements. - Bachelor's degree in Computer Science, Software Engineering, or a related field or equivalent experience. - 5–8+ years of software and platform engineering experience, with a strong track record of designing and operating large-scale distributed systems. - Experience building platform infrastructure and backend systems that serve multiple products and engineering teams. - Experience in Rust, with expertise in designing and developing high-performance systems. All our performance-critical areas are developed in Rust. - Demonstrated expertise in database architecture and scalable data systems, including distributed databases, storage and indexing systems, data modeling and schema design, high-throughput and low-latency data access patterns. - Experience designing systems that support AI and machine learning workloads. - Familiarity in workflow engines, execution runtimes, and AI orchestration frameworks. - Growth mindset and low ego - you’re eager to pick up new tools and technologies, learn from others, and be open to changing course when it’s right. - Drive to foster a culture of collaboration, psychological safety, and technical excellence. - Strong advocate for inclusion in technical discussions at all levels. Requirements - Start-up experience or comfort in 0→1 product environments. - Familiarity with Kubernetes is a plus. - Understanding of DevOps practices and CI/CD pipelines for platform deployment. Benefits - Compensation Information: $170,000 — $230,000 USD Equal Opportunity Statement Legion provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

• Serve as the technical lead for APAC and help build the region • Embed with enterprise customers, taking them from first evaluation to production deployment • Own how Stacklok lands with every customer in the region • Shape technical direction across APAC, establish deployment patterns, and mentor engineers • Solve problems determining whether AI initiatives successfully reach production • Adapt forward deployed work to how local customers operate

• Design, develop, and maintain scalable data pipelines and ELT/ETL workflows on Snowflake, integrating data from diverse internal and external sources. • Architect and optimize Snowflake data models, schemas, and warehouses for performance, reliability, and cost efficiency. • Implement and enforce data governance, security, role-based access controls, and data quality standards across the platform. • Monitor and tune warehouse performance, query execution, and resource consumption to control costs and meet SLAs. • Build and maintain CI/CD pipelines for data infrastructure using tools such as dbt, Git, and orchestration frameworks (e.g., Airflow). • Leverage advanced Snowflake features — Snowpipe, Streams, Tasks, Time Travel, Dynamic Tables, and Snowpark — to deliver near-real-time and automated data solutions. • Collaborate with analysts, data scientists, and business stakeholders to translate requirements into robust data solutions. • Mentor junior engineers, conduct code reviews, and establish best practices for data engineering across the team.

• Architect, build, and maintain scalable ETL/ELT pipelines on the Databricks Lakehouse Platform using PySpark, Spark SQL, and Delta Lake. • Design and implement medallion (bronze/silver/gold) data architectures and enforce data quality, governance, and lineage standards. • Optimize Spark jobs and cluster configurations for performance and cost, including partitioning, caching, and autoscaling strategies. • Implement and manage Unity Catalog for access control, data governance, and cross-workspace asset sharing. • Build and orchestrate workflows using Databricks Workflows, Delta Live Tables, and CI/CD pipelines. • Collaborate with data scientists, analysts, and business stakeholders to translate requirements into reliable data products. • Establish engineering best practices, conduct code reviews, and mentor junior data engineers. • Monitor production pipelines, troubleshoot failures, and drive root-cause analysis and continuous improvement.