• Own and enforce configuration control across hardware, software, and technical data • Define, implement, and continuously improve configuration management (CM) processes, standards, and tools • Develop and maintain Configuration Management Plans (CMPs) at program and enterprise level • Serve as system owner and administrator for the Product Lifecycle Management (PLM) system • Establish and control part numbering schemes, bills of materials (BOMs), item attributes and lifecycle states • Own the Engineering Change Order (ECO) process end-to-end and establish and maintain product baselines (design, build, as-built) • Lead the Configuration Control Board (CCB): Drive decisions, prevent uncontrolled change, and ensure closure of actions • Support and prepare for configuration audits, manufacturing and readiness reviews, and design reviews • Compile and validate End Item Data Packages (EIDPs) for delivered products • Interface across Engineering, Manufacturing, Supply Chain, Quality, and Program Management to maintain a single authoritative data source

Role Description We are looking for a Manager – AI & Machine Learning Engineering to lead a team of ML engineers in developing and deploying high-impact machine learning solutions across core enterprise functions including Sales, Service, Finance, Order Fulfillment, and Supply Chain. This role requires a strong mix of technical leadership, people management, and strategic alignment to guide ML engineers from ideation through production deployment. You’ll play a key role in shaping the AI/ML delivery roadmap, establishing scalable engineering practices, and driving value through predictive models integrated into critical business workflows. Responsibilities - Team Leadership and Talent Development - Manage, coach, and grow a high-performing team of machine learning engineers, promoting a culture of innovation, collaboration, and continuous learning. - Provide technical direction, architectural oversight, and career mentorship. - Define team objectives and success metrics aligned with enterprise priorities. - Program Execution and Delivery - Drive the successful execution of ML use cases such as customer churn prediction, upsell opportunity scoring, demand forecasting, and operational risk detection. - Work closely with data science, data engineering, product, and business stakeholders to define and deliver scalable ML solutions. - Oversee delivery timelines, model development, deployment readiness, and feedback integration. - ML Engineering and MLOps Strategy - Establish best practices in model development, deployment, and monitoring, using tools like MLflow, SageMaker, Azure ML, Airflow, or Kubeflow. - Guide the team in implementing CI/CD for ML pipelines, model versioning, feature stores, and performance monitoring. - Champion a strong foundation in software engineering, code quality, and reusability in ML development. - Functional & Cross-Domain Focus - Align ML efforts with key business domains such as Sales (lead scoring, renewals), Service (case triage), Finance (forecasting), Order Fulfillment (ETA, risk), and Supply Chain (inventory planning, logistics optimization). - Collaborate with business owners to prioritize high-impact ML use cases and ensure adoption and value realization. - Technology & Architecture Oversight - Partner with data platform and infrastructure teams to scale ML solutions using Snowflake, Datarobots, and enterprise cloud platforms (AWS, Azure, GCP). - Ensure ML models integrate seamlessly with business systems such as Salesforce, Oracle Fusion Cloud, and other operational tools. Qualifications - Required: - 8+ years of experience in machine learning, data science, or engineering roles, with 3+ years in a technical leadership or management capacity. - Proven experience building and deploying machine learning solutions in production environments. - Hands-on background with Python, ML frameworks (scikit-learn, PyTorch, TensorFlow), and orchestration tools. - Strong understanding of MLOps practices, model lifecycle management, and pipeline automation. - Experience working with cross-functional stakeholders to deliver ML-powered business solutions. - Preferred: - Experience supporting business functions such as Sales, Finance, or Supply Chain with applied ML. - Familiarity with cloud platforms (AWS, Azure, or GCP) and enterprise data tools (Snowflake, dbt, Matillion). - Exposure to enterprise platforms such as Oracle Fusion Cloud, Salesforce, or ServiceNow. Company Description

• Забезпечити передбачуваний, прозорий і керований процес доставки продукту від ідеї до релізу, синхронізувавши роботу продукту та інженерії • Створення єдиної системи пріоритизації, планування та execution, яка дозволяє командам працювати з фокусом • Мінімізування хаосу і забезпечення досягнення бізнес-цілей у визначені терміни • Побудова та впровадження end-to-end delivery процесу (intake, пріоритизація, планування, execution, реліз) • Формування та підтримка єдиного беклогу і роадмапу разом із Product Owner’ами, забезпечення прозорості та узгодженості пріоритетів • Налаштування та розвиток Jira як основного інструменту управління задачами (структура, workflow, правила, звітність) • Управління крос-командними залежностями, ризиками та блокерами, забезпечення безперервного потоку delivery • Контроль ефективності команд через управління WIP, балансування навантаження та покращення планування • Впровадження та моніторинг метрик delivery (lead time, cycle time, throughput, predictability) для прийняття рішень на основі даних

• Oversee end-to-end execution of customer implementations and onboarding projects across all product lines • Ensure projects are delivered on time, within scope, and aligned with defined processes and customer expectations • Act as the primary escalation point for delivery-related issues and drive timely resolution • Monitor projects’ health, risks, and dependencies across the portfolio • Lead and manage a cross-functional Delivery team, including Project Managers, techOMS specialists, techSHIP specialists, and Integration specialists • Provide day-to-day guidance, prioritization, and support to ensure effective execution across all projects • Coach and support team members to improve performance and execution quality • Conduct performance evaluations and provide ongoing feedback • Identify hiring needs and support recruitment and onboarding • Implement and enforce delivery processes, methodologies, and best practices • Identify operational gaps and areas for improvement and propose solutions • Standardize onboarding and implementation workflows to improve efficiency and consistency • Ensure proper documentation of processes, projects, and knowledge sharing • Own and track Delivery team KPIs, including timelines, quality, and customer outcomes • Ensure accountability and productivity across all roles within the Delivery function • Monitor performance metrics and take corrective actions when needed • Work closely with Customer Success, Support, Product, and Engineering teams to ensure smooth execution • Ensure alignment between delivery activities and broader Customer Operations objectives • Provide regular updates to leadership on delivery status, risks, and performance • Manage and control the Delivery function budget • Allocate resources effectively based on project demand and team capacity • Ensure efficient utilization of the team while maintaining delivery quality