• Design, develop, and deploy LLM-powered applications • Lead the definition, development, and delivery of machine learning models • Lead the testing, deployment, and ongoing maintenance of AI systems • Optimize AI systems for performance, scalability, latency, and cost • Partner with data and engineering leads to architect AI-powered systems • Establish evaluation frameworks, monitoring, and observability

• Architect and maintain AWS demo environments using services such as EC2, ECS/Fargate, Lambda, S3, RDS, DynamoDB, IAM, KMS, API Gateway, CloudWatch, and AWS Glue • Develop Python-based solutions leveraging libraries like NumPy, Pandas, Scikit-learn, and others for data processing and automation • Build and manage Terraform modules for automated provisioning, environment resets, and decommissioning • Integrate AWS GenAI services (Bedrock, SageMaker) for real-time demo enrichment and scenario automation • Collaborate with Demo COE and Product SMEs to design and implement custom demo flows • Implement RBAC, feature toggles, and configuration management using AWS Systems Manager and AppConfig • Ensure environmental health through automated monitoring, alerting, and load testing (CloudWatch, ALB, X-Ray) • Support automated QA gates, metrics collection, and post-demo knowledge management • Provide secondary support for data engineering tasks (AWS Glue, Redshift, Athena) for ingestion and transformation • Contribute to hybrid cloud demos and integrations with Azure as needed

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description The AI Operations Analyst at Perforce will be responsible for optimization of the internal tools across the organization. This role will focus on leveraging automation and artificial intelligence to streamline processes, enhance efficiency, and enable scalable growth. You will bring experience in process mapping, a strong grasp of industry trends, and a proven ability to develop system improvements across large-scale teams and environments. - Optimization: Analyze cross-functional processes to identify opportunities for improvement through automation and AI-driven solutions. Provide direction to ensure long-term scalability, maintainability, and security of systems. - Project Leadership: Lead internal AI initiatives, ensuring successful integration into existing systems and workflows. Evaluate and recommend emerging technologies, fostering innovation and competitive differentiation. - Business Practice Scalability: Create central knowledge base for all AI to ensure no duplication and business debt. Solutions are scalable, secure, and efficient to support global operations and future growth. Guide critical decisions for high-impact projects and resolve cross-domain challenges. - Stakeholder Engagement: Work with cross-functional teams to ensure adoption and maximize the impact of implemented solutions. This role bridges the gap between technical teams, business stakeholders, and operations, ensuring the successful execution of AI-driven projects that enhance efficiency, forecasting, automation, and decision-making. - Organizational Readiness: Curate role personas and partner across the enterprise to build AI learning opportunities & showcase use cases. Lead organizational change management. Lead organizational learning initiatives to upskill the enterprise. Enable organization in all things AI. Collaborate with developers, data engineers, and data scientists to deliver project execution goals. - Measurement: Validate optimization and cost savings across the enterprise through implementation and adoption of automation and artificial intelligence. Ensure existing or proposed AI technologies meet measurement goals and will deliver value to the business. - Industry Trends: Keep the corporation abreast of trends in AI and opportunities to create optimization. Bring awareness to the advancements and the long-term direction of these AI technologies in balance with their current state existing capabilities. Qualifications - BS Computer Science, Software Engineering, or related field. - Background in developing AI programs and driving adoption across enterprise platforms such as Microsoft Copilot, Salesforce Agent Force, Gong and related AI tools. - Strong understanding of data models, objects, workflows, and reporting capabilities. - Experience with requirement gathering, process documentation, and translating business needs into technical specifications. - Demonstrated ability to map processes and influence simplification leveraging automation and AI. Hands-on experience creating proof of concepts. - Demonstrated ability to lead through influence in a complex organizational structure and communicate effectively with stakeholders. - Strong track record of driving technical strategy, standards, and cross-functional alignment at scale. - Passion for mentoring, thought leadership, and advancing excellence across the company. - Able to demonstrate leveraging AI effectively and ethically. Requirements - $109,850 - $157,900 a year Benefits - Medical, dental, vision, retirement benefits - Life insurance - Wellness programs - Total time off - Other employee perks that may be offered by Perforce from time to time

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Frontier Technology Inc. (FTI) is seeking an AI/ML Software Engineer to design, build, and deploy secure, scalable software and data systems that support mission operations, analytics, and simulation environments. This role is for a hands-on engineer and someone who loves writing code, building systems end-to-end, and solving real-world technical challenges in secure, distributed environments. Responsibilities - Software Design & Development - Design and implement APIs, data pipelines, and simulation runtime logic that connect and enable mission applications. - Develop software using modern programming languages such as Java, Python, C++, or TypeScript/Angular. - Write clean, testable, and maintainable code following secure coding and software engineering best practices. - Build and integrate modular microservices to improve scalability, maintainability, and interoperability. - Cloud & Containerized Environments - Build and deploy containerized, cloud-native services using Docker, Kubernetes, and CI/CD pipelines (GitLab, Jenkins, or equivalent). - Implement Infrastructure-as-Code and automation scripts to accelerate deployment and configuration management. - Contribute to secure deployments across hybrid or disconnected environments (IL4–IL6, AWS GovCloud, or on-prem). - Systems Integration & Distributed Computing - Develop distributed systems and data integration frameworks using message buses such as Kafka or Redis. - Engineer data flow between analytic, AI, and simulation components to support real-time mission use cases. - Collaborate with system engineers and architects to ensure interoperability across software ecosystems. - Data & Analytics Integration - Build and manage databases (PostgreSQL, MongoDB, graph DBs) and model complex data relationships. - Develop data services that feed analytics pipelines or integrate AI/ML outputs into runtime systems. - Work with serialization and exchange formats such as JSON, Protobuf, GeoJSON, or KML. - Security, Testing & Sustainment - Write, test, and deploy software within secure or classified environments. - Automate testing and monitoring to ensure performance, reliability, and repeatable deployments. - Support the transition of prototypes to operational systems, focusing on maintainability and observability. Qualifications - Must be a U.S. citizen and be willing to obtain and maintain a security clearance, as needed. - 6-10+ years of professional software engineering experience. - 3+ years of professional experience with DevSecOps, Zero-Trust, or ATO/RMF processes in Department of Defense (DoD/DoW) environments. - Strong full-stack or systems engineering background. - Proficiency in one or more of the following languages: Java, Python, C++, or TypeScript/Angular. - Experience building containerized, cloud-native solutions using Docker, Kubernetes, and CI/CD pipelines. - Complete understanding of distributed systems and message buses (Kafka, Redis, etc.). - Experience developing or integrating analytics and AI models into production systems. Preferred Qualifications - Experience deploying code in IL4–IL6 or edge/disconnected environments. - Familiarity with databases such as PostgreSQL, MongoDB, or graph databases. - Knowledge of Infrastructure-as-Code (Terraform, CloudFormation, or CDK). - Bachelor’s degree in Computer Science, Software Engineering, or a related technical field. - Active Secret clearance preferred; ability to obtain one is required.