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.

• Design, develop, and deploy production-ready AI/ML models and applications that address critical business problems. • Lead AI Transformation in the region by influencing, educating, and evangelizing the use of Data Science and AI. • Drive engagement and adoption of Data Science and AI solutions, acting consultatively to define regional strategy. • Own the full ML lifecycle: design data pipelines, feature engineering, training, evaluation, deployment, and model monitoring. • Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions. • Implement MLOps best practices, including model versioning, CI/CD pipelines, and automated re-training. • Translate complex business requirements into AI/ML solutions with clear success metrics. • Conduct code reviews and establish engineering standards for AI projects. • Evaluate and integrate emerging AI technologies (LLMs, GenAI, RAG systems) into the company ecosystem. • Lead POCs and MVPs using design thinking methodologies to validate solution feasibility. • Optimize model performance, latency, and cost efficiency in production systems. • Contribute to Syngenta’s AI platform capabilities and to libraries of reusable components.

• Développer et optimiser des workflows agentiques complexes et multi-étapes permettant à l'IA d'exécuter des tâches et des prises de décision autonomes. • Concevoir et maintenir des systèmes de génération augmentée par récupération (RAG) pour connecter les LLM aux données propriétaires de manière sécurisée et précise. • Évaluer et configurer divers LLM en fonction des contraintes spécifiques aux projets, telles que la latence, le coût et la confidentialité des données. • Établir des normes de conception d'instructions pour garantir que les sorties des modèles sont fiables, sécurisées et adaptées au contexte. • Concevoir des stratégies de mémoire à court et long terme pour les systèmes agentiques.

• Own end-to-end delivery: Design and build full-stack features from UI to backend to deployment. • Ship real AI systems: Implement RAG, tool-calling, agent workflows, structured prompting, evals, and guardrails. • Move fast, responsibly: Prototype quickly, then harden for security, reliability, and cost control. • Use AI as a force multiplier: Work fluently with tools like Cursor, Claude Code, Windsurf, or Copilot to accelerate delivery while maintaining code quality. • Make technical calls: Choose patterns, trade-offs, and scope boundaries without waiting for permission. • Collaborate tightly: Work directly with strategists, clients, and other senior engineers to unblock progress. • Light DevOps ownership: Configure environments, CI/CD, secrets, and deployments (with support when complexity grows).