Role Description The Sterling Federal Presales Leader will lead a team of highly effective sales engineers to support and enable Sterling’s Federal sales across all DoD, IC, and Global segments. This is a highly visible position at Sterling that will require cross-functional leadership to execute technical sales strategies within the Federal government. This leader will attract, recruit, retain, and develop engineers of various capabilities and align them to federal segments at Sterling and be a point of escalation for all teams. The Federal Presales leader will develop technical sales strategies of deeply complex and large organizations and articulate the value of Sterling to customers and industry peers. We are looking for a high caliber technical thought leader in the areas of server, storage, cloud, cyber, AI/ML, compute & infrastructure that can accelerate our growth by inspiring and enabling individual contributors to perform at high levels. Qualifications - Must have Federal (Civilian or DoD) experience. - Strong aptitude and understanding of a wide breadth of information technology, products, tools, services, and solutions. - Deep expert level experience with infrastructure solutions provided by Dell, Cisco, HPE, VMware, Microsoft, Red Hat, and more. - Understanding of the tactical and strategic elements of Solutions Consulting as a technical VAR or vendor. Requirements - Developing and executing department strategies that win. - Develop strategies for your team, build plans, execute, measure, and grow. - Work with internal resources to develop programs for execution of your strategy. - Align strategies to corporate level strategies. - Properly align engineers based on skillset and demand from various sales segments. - Ensure technical strategies within the company make sense and will be profitable. - Develop and maintain relationships with key customers and vendor stakeholders. - Work with other cross-functional departments to push initiatives and be an advocate for our customers. - Participate in account planning and QBRs with sales and sales leadership. - Balance customer advocacy and need with what’s best for Sterling as a business. - Plan staffing levels to meet Sterling’s strategic objectives. - Work with HR staff to recruit, interview, select, hire, and employ an appropriate number of employees. - Lead employees using a performance management and development process. - Clearly communicate job expectations; plan, monitor, and appraise job results against these expectations. - Provide effective performance feedback through employee recognition, rewards, coaching, counseling, and disciplinary action. - Develop, coordinate, and enforce systems, policies, procedures, and productivity standards. - Coach, mentor and develop team members; provide career development planning and opportunities. - Successfully onboard new employees and train them to be fully productive team members. - Consciously create a workplace culture that is consistent with Sterling’s overall mission and core values. - Maintain transparent communication within and between teams. - Proactively solicit and listen to employee feedback and convey this to other leaders or individuals as appropriate. - Resolve conflicts between individuals and teams. Benefits - Opportunity to work in a highly visible position. - Supportive workplace culture that fosters teamwork and unity. - Career development planning and opportunities. - Performance recognition and rewards. Core Technical Skillset Requirements - Virtualization: strong understanding of virtualization technologies, including hypervisors such as VMware, Hyper-V, KVM, and Citrix. - DR/Backup: strong understanding of backup and disaster recovery strategies from vendors such as Veeam, Zerto, Dell, Commvault, Rubrik, and Cohesity. - Storage: knowledge of storage architecture, including storage area networks (SANs), network-attached storage (NAS), and software-defined storage. - Network Architecture: in-depth knowledge of network architecture, including software-defined networking (SDN) and network virtualization. - Application & Automation: experience with automation tools such as Ansible, Puppet, and Chef. - Security: strong understanding of security principles, including authentication, authorization, and encryption. - Cloud: familiarity with cloud computing technologies, including public, private, and hybrid clouds. - Cost Optimization: understanding of cost optimization techniques. - DevOps: familiarity with DevOps practices and tools. - Services: MSP and Deployment experience.

• Design, develop, and deploy ML solutions ranging from traditional ML applications (classification, clustering, recommendations) to LLM-based systems, including document parsing, data extraction, RAG pipelines, and LLM agents • Write clean, maintainable, production-quality Python code that integrates smoothly with existing engineering and deployment infrastructure • Work with large datasets to clean, preprocess, and analyze data, ensuring data quality and integrity • Implement and optimize algorithms using best practices in machine learning, deep learning, and statistical analysis • Collaborate with business stakeholders to understand requirements and deliver data-driven solutions that provide actionable insights • Develop and maintain scalable pipelines and infrastructure for data processing and model training, versioning, deployment, and monitoring • Evaluate the performance of machine learning models, including LLM-specific evaluation approaches, and tune models for optimal performance • Communicate findings, insights, and model performance to both technical and non-technical audiences • Continuously stay updated on the latest trends, technologies, and best practices

• Drive execution and build a culture of innovation • Manage and inspire a multi-functional team • Collaborate with cross-functional stakeholders to understand business needs • Formulate and complete end-to-end analysis including data gathering, analysis, and recommendations • Contribute to a wide variety of research projects using statistical analysis and modeling • Develop and implement unsupervised machine learning and deep learning algorithms for predicting asset failure • Develop computer vision models for defect detection and classification • Build generative AI solutions for improving operational productivity and accelerating engineering feature releases • Design and evaluate experiments for customer retention based on statistical analysis

• Profile the anonymized lake hands-on — interrogate tens-of-millions-of-row tables and reproduce and validate the team's existing descriptive statistics, so every number is traceable to source. • Build and validate the core risk models yourself: PD, delinquency / roll-rate, early-warning, segmentation and scorecards. • Stand up the model-validation discipline that makes outputs audit-defensible: train / test / out-of-time splits, Gini / AUC / KS, calibration, stability (PSI), backtesting and full model documentation. • Define feature logic with the Data Engineer and write it yourself in SQL / dbt / Python; specify the harmonized definitions the semantic layer must serve. • Prototype and validate the natural-language insight layer; check answer correctness and add guardrails. • Run a credit-policy / cut-off analysis showing where the client could tighten policy or reduce delinquency — the concrete insight their own clients keep asking for. • Lead a small pod (Data Engineer, client's junior offshore data people): set tasks, review work, be the quality bar and the human-in-the-loop. • Front the client's data leadership: present findings, explain methodology to non-technical executives, and shape the phased roadmap / SoW.