• Define a clear CSP prioritization strategy (who first, why, and how). • Build a repeatable hyperscaler engagement model suitable for Series A scale. • Align internal expectations on what CSPs will—and will not—do at this stage. • Drive hyperscaler co-sell motions with field sellers and solution architects. • Enable CSP teams to position the platform as: • A cloud-aligned GRC solution • A driver of AI and data workloads • Support joint account planning and opportunity mapping. • Own end-to-end CSP marketplace execution: • Listings • Pricing models • Private offers • Enterprise agreements • Ensure marketplace deals accelerate—not slow—enterprise sales cycles. • Align platform capabilities to measurable cloud consumption drivers: • AI/LLM workloads • Continuous control monitoring • Data ingestion and analytics • Partner with Product and Engineering on: • Reference architecture • CSP-native integrations • Roadmap alignment • Build relationships with: • Global CSP alliance leaders • Regional sales leaders • Industry solution teams • Navigate CSP partner programs, incentives, MDF, and funding mechanisms. • Represent the company in CSP executive briefings and partner councils.

• Lead, mentor, and grow a high-performing team of software engineers focused on compute and platform engineering • Drive architectural direction for the compute platform with an emphasis on reliability, scalability, and developer experience • Contribute hands-on to design, implementation, and optimization of core compute services (orchestration, provisioning, scaling, runtimes) • Collaborate closely with Infrastructure, Security, and Backend teams to define priorities and ensure smooth integrations • Conduct regular 1:1s, performance reviews, and technical coaching • Foster a culture of ownership, engineering excellence, and continuous improvement • Translate strategic objectives into actionable execution plans and ensure timely delivery • Influence platform evolution with pragmatic, forward-looking technical decisions • Champion best practices across software development, automation, and operational excellence

• Own the operation and evolution of OpenStack-based private clouds, focusing on reliability, upgrades, automation, and solving real infrastructure problems across compute, network, and storage. • Run and evolve an OpenStack private cloud (ops, upgrades, stability) • Automate infrastructure work • Troubleshoot infra issues across Linux, virtualization, storage, and networking • Improve observability, reliability, and security baseline

Company Description Established in 2003, JTSi is an IT & Engineering Services provider with years of documented experience in the Information Technology and Engineering services field.  JTSi has a proven track record for successfully delivering mission-critical Professional IT Services to the Public Sector.  JTSI SAP/ERP team delivers high-value business solutions to its clients by clearly understanding their core business problems.  We deliver quality services at equitable rates and focus on constant improvement in all areas of our operation, with a strong commitment to meeting our Customers’ needs.  We view ourselves more as a Business Partner than merely a consulting services provider.  US Army ERP Services Contract award to JTSi for a ceiling value of $846M is one of the most prestigious awards of the time.  At JTSi “Customer Is Always First” and partnering is our path to customer satisfaction - We do what we say. Visit us at www.jtsusa.com Job Description Deep Knowledge on Open Source Technologies for Distributed Data processing, analysis of big data sets including writing jobs to process vast amounts of data and ETL processing, required expertise is over tools such as: Apache Hadoop, MapReduce, Pig, Spark, Databricks, Hive Deep Knowledge on Open Source Technologies to perform fast, interactive SQL queries at scale over structured or unstructured big data sets, required expertise is over tools such as: Apache Hive LLAP, Spark SQL, Phoenix Deep Knowledge on Open Source Technologies to enable ingestion and processing of millions of streaming events per second: Apache Kafka, Apache Storm, Apache Spark, Apache HBase Deep Knowledge on Performance Analysis and Optimization and operation of Apache Hadoop, Spark, Databricks and the others mentioned above. Deep Knowledge on Open Source Technologies for Machine Learning such as: MLib Strong expertise in ETL/ELT approaches and expertise in technologies such as: Azure Data Factory and SQL Server Integration Services. Software Development skills to write code on Java, Spark Scala, PySpark, Python Qualifications