• The Netcracker OSS Developer / Configurator is responsible for the configuration, customization, and implementation of Netcracker OSS solutions supporting network operations and service lifecycle management • This role focuses on core OSS domains including Network Inventory, Service Provisioning, Fault Management, Performance Monitoring, Configuration Management, and Service Assurance • Configure and customize Netcracker OSS modules including: Network Inventory, Service Provisioning, Fault Management, Performance Monitoring, Configuration Management, Service Assurance • Develop workflows, adapters, rules, and orchestration logic to support service activation and lifecycle management • Implement service models, resource models, and network topology configurations • Configure event correlation, alarm rules, and monitoring thresholds • Customize dashboards, reports, and operational views for NOC and service operations teams • Design and implement integrations between Netcracker OSS and: Network elements (via SNMP, TL1, REST, SOAP, etc.), EMS/NMS platforms, BSS systems, Third-party monitoring tools • Support provisioning flows across multi-technology environments (IP, MPLS, Fiber, Wireless, etc.) • Implement automation scripts and orchestration logic to reduce manual intervention • Support northbound and southbound interface configuration and troubleshooting • Participate in solution design and technical impact assessments • Perform unit testing and support system, integration, and user acceptance testing • Troubleshoot issues related to inventory inconsistencies, provisioning failures, alarm storms, and performance degradation • Support production deployments and provide post-go-live stabilization support • Maintain configuration and technical documentation

• This position is responsible for designing, managing, and coordinating the development of technical training for customers, F5 employees, and channel partners in all F5 theatres. • Creating the vision and solution framework to fulfill the defined business requirements while working within established guidelines and templates. • Defining training requirements, workflow, processes, scope, and curriculum of the solution or series. • Managing and communicating the project plan, schedule, and status. • Designing and developing new technical training courses / learning path for various customer audiences. • Completing training needs analysis, audience analysis, and defining project scope and requirements. • Determining pre-requisites and appropriate delivery methodology for new training content. • Managing multiple course development projects including scope, dev-plan, schedule, milestones, and status. • Delivering technical training and Train-the-Trainer classes. • Auditing class delivery to ensure consistency and discover areas for improvement.

• Our consultants on the Defense & Security AI & Data team help clients maximize the value of their data and automate business processes. • Design, build, and optimize data pipelines and Palantir Foundry-based workflows. • Develop scalable data models and ontology structures within Palantir Foundry. • Collaborate with cross-functional teams to translate operational requirements into robust Foundry solutions. • Manage ETL functions and develop, build, test, and maintain scalable data pipeline architectures and tools. • Work with data integration and management tools and databases. • Provide and prepare data to enable data science and machine learning. • Ensure data governance and quality assurance standards are met. • Summarize and present information and reports to the team and make recommendations.

Role Description You’ll be a generalist responsible for building and running large-scale data, machine learning, and agentic systems. The focus is operational ML/AI, including agentic systems and geospatial data pipelines. You should be comfortable owning the full lifecycle: from data ingestion and distributed processing to model development, deployment, and monitoring. This role requires the ability to iterate quickly from initial concept to a robust, production-ready solution. Key Responsibilities - Take ownership of the end-to-end AI/ML lifecycle, focusing on complex and messy data, evaluating different approaches, deploying robust models, and handling cost vs performance tradeoffs. - Implement and integrate large-scale, agent-based systems with access to external systems, building solutions from the ground up and integrating them with existing infrastructure. - Establish observability for pipelines, models, and agents (metrics, tracing, alerting). - Collaborate with product and customer teams to drive revenue. - Strong experience with distributed data processing, particularly Spark and SQL. - Proven expertise in building production machine learning systems, including working with large, wide datasets, effective training, deployment, and monitoring. - Experience designing and deploying task-oriented AI agents and working with coding agents. - Experience working with cloud services across data, compute, and ML. - Strong communication abilities, including code architecture and documentation, enabling any technical team member to troubleshoot and contribute easily. Languages - Scala - Python Tools / Frameworks - Spark - AWS Sagemaker / Bedrock - Kubernetes Nice to Haves - Startup experience or growing projects from 0 to production in a larger organization. - Experience with large geospatial datasets, formats, and indexing strategies. - Experience building operational AI agents that work at scale (millions of separate, complex tasks including web research). - Experience with fine-tuning, distilling, and self-hosting LLM models. - Experience in traditional ML, focusing on working with messy data and robust evaluation of model approaches. - Proficiency with CI/CD, infrastructure as code, and containerization. What Success Looks Like - ML/AI models deployed with robust monitoring and significant customer impact. - Agentic workflows improving internal/external operations. - Infrastructure that is stable, observable, and automated. - Successful iteration and delivery of new ML/AI products from concept to production. - Ability to contribute to existing geospatial pipelines directly or through the use of AI.