FTI - Frontier Technology Inc.

• Architect and integrate hybrid AI systems that combine traditional machine learning, deep learning, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines. • Design and deploy scalable AI architectures including APIs, microservices, and model-serving frameworks that integrate seamlessly with analytic, simulation, or operational systems. • Lead the full AI/ML lifecycle — from data ingestion and feature engineering through training, deployment, and sustainment within secure DoD environments (IL5/IL6, ATO, GovCloud). • Engineer event-driven data pipelines and feature stores for both structured and unstructured data, including text, imagery, and simulation outputs. • Ensure Responsible AI practices by embedding traceability, explainability, and confidence scoring into deployed systems. • Implement and maintain MLOps pipelines (MLflow, Kubeflow, Airflow, Docker/Kubernetes) to support continuous integration, retraining, and drift detection. • Transition R&D prototypes into production, optimizing for mission constraints such as limited compute, edge environments, or disconnected operations. • Provide technical leadership and mentorship, setting standards for model quality, architectural design, and ethical AI deployment across programs. • Collaborate across engineering, data, and modeling teams to unify FTI’s AI portfolio, ensuring interoperability and reuse across mission systems. • Support proposal and solution development, providing technical inputs for AI/ML architectures, data strategies, and Responsible AI assurance frameworks.

• Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives. • Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain. • Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration). • Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid). • Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services. • Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT). • Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy. • Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots. • Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs. • Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.

• Architect and integrate hybrid AI systems. • Design and deploy scalable AI architectures. • Lead the full AI/ML lifecycle. • Engineer event-driven data pipelines and feature stores. • Ensure Responsible AI practices. • Implement and maintain MLOps pipelines. • Transition R&D prototypes into production. • Provide technical leadership and mentorship. • Collaborate across engineering, data, and modeling teams. • Support proposal and solution 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 • 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 • 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 • Build and manage databases (PostgreSQL, MongoDB, graph DBs) and model complex data relationships • Write, test, and deploy software within secure or classified environments.

• 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. • 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). • 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. • 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. • 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.

• Architect and deploy secure, distributed platforms supporting modular, multi-service software environments. • Design and integrate APIs, microservices, and data pipelines that enable interoperability across mission, simulation, and analytical systems. • Define and enforce architectural principles, modularity standards, and software best practices across development teams. • Engineer solutions that bridge cloud, on-prem, and classified enclaves, ensuring interoperability and compliance. • Engineer cloud-native solutions in AWS GovCloud or hybrid IL4–IL6 environments with strict security and accreditation requirements. • Implement Infrastructure-as-Code (Terraform, CloudFormation, or CDK) to automate provisioning, configuration, and accreditation workflows. • Build and maintain CI/CD pipelines that embed security, compliance, and automated testing from development through deployment. • Ensure all platform designs support zero-trust principles, multi-enclave deployment, and repeatable accreditation (ATO) processes. • Design or oversee large-scale data ingestion, transformation, and orchestration environments (Kafka, Airflow, Spark, or Databricks). • Shape data architectures that balance throughput, latency, and cost across distributed mission environments. • Enable secure and efficient data access and synchronization across analytical, AI-enabled, and simulation workloads. • Transition software and platforms from prototype to production, ensuring maintainability, observability, and configuration consistency. • Establish automated testing, release governance, and performance monitoring standards for ongoing sustainment. • Partner with program and product leadership to align delivery with mission lifecycle requirements. • Engineer architectures for redundancy and resilience under degraded or disconnected operational conditions. • Design and maintain secure enclave architectures, boundary controls, and cross-domain integration solutions. • Collaborate with cybersecurity and ISSM teams to support ATO and ongoing accreditation automation. • Integrate emerging technologies, open standards, and modern frameworks into operational systems. • Collaborate with AI/ML, data, and simulation teams to enable model integration and inference services within production software (familiarity with MLOps concepts preferred). • Evaluate new tools and frameworks to improve developer productivity, performance, and automation efficiency. • Lead by example as a hands-on technical authority, guiding design and implementation decisions. • Mentor software engineers and architects to raise technical standards, code quality, and architectural maturity. • Partner with fellow Distinguished Engineers and organizational leaders to align platform strategy with enterprise innovation goals. • Communicate complex technical concepts clearly to both executive and engineering stakeholders.

• Drive Air Force market growth. • Lead all growth activities within the Air Force pipeline, developing and executing account strategies that position the company’s solutions as mission-relevant capabilities. • Identify future mission problems and shape solutions. • Engage operational users, program offices, and mission leaders to understand emerging challenges and shape solutions that align the company’s platforms and capabilities to those needs. • Execute a solution sales approach. • Move beyond traditional opportunity tracking by building demand through mission engagement, demonstrations, pilots, prototypes, and early adoption programs. • Identify, qualify, and mature opportunities from early mission engagement through capture and proposal stages across multiple acquisition pathways (SBIR, OTA, CSO, MAC, IDIQ, and other vehicles). • Build lasting and trusted strategic relationships across Air Force including operational units, acquisition offices, laboratories, and mission support organizations. • Work closely with technology, capture and proposal teams, operations, and shared services to ensure opportunities are properly shaped, resourced, and executed. • After initial wins drive expansion within Air Force organizations by identifying additional mission users/applications and scaling adoption across commands and programs.

• Drive Air Force market growth. • Lead all growth activities within the Air Force pipeline, developing and executing account strategies that position the company’s solutions as mission-relevant capabilities. • Identify future mission problems and shape solutions. • Engage operational users, program offices, and mission leaders to understand emerging challenges and shape solutions that align the company’s platforms and capabilities to those needs. • Execute a solution sales approach. • Identify, qualify, and mature opportunities from early mission engagement through capture and proposal stages across multiple acquisition pathways (SBIR, OTA, CSO, MAC, IDIQ, and other vehicles). • Build lasting and trusted strategic relationships across Air Force including operational units, acquisition offices, laboratories, and mission support organizations. • Drive expansion within Air Force organizations by identifying additional mission users/applications and scaling adoption across commands and programs.

• Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives. • Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain. • Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration). • Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid). • Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services. • Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT). • Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy. • Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots. • Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs. • Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.

• Architect and deploy secure, distributed platforms supporting modular, multi-service software environments. • Design and integrate APIs, microservices, and data pipelines that enable interoperability across mission, simulation, and analytical systems. • Define and enforce architectural principles, modularity standards, and software best practices across development teams. • Engineer solutions that bridge cloud, on-prem, and classified enclaves, ensuring interoperability and compliance. • Engineer cloud-native solutions in AWS GovCloud or hybrid IL4–IL6 environments with strict security and accreditation requirements. • Implement Infrastructure-as-Code (Terraform, CloudFormation, or CDK) to automate provisioning, configuration, and accreditation workflows. • Build and maintain CI/CD pipelines that embed security, compliance, and automated testing from development through deployment. • Ensure all platform designs support zero-trust principles, multi-enclave deployment, and repeatable accreditation (ATO) processes. • Design or oversee large-scale data ingestion, transformation, and orchestration environments (Kafka, Airflow, Spark, or Databricks). • Shape data architectures that balance throughput, latency, and cost across distributed mission environments. • Enable secure and efficient data access and synchronization across analytical, AI-enabled, and simulation workloads. • Transition software and platforms from prototype to production, ensuring maintainability, observability, and configuration consistency. • Establish automated testing, release governance, and performance monitoring standards for ongoing sustainment. • Lead by example as a hands-on technical authority, guiding design and implementation decisions. • Mentor software engineers and architects to raise technical standards, code quality, and architectural maturity.