FTI - Frontier Technology Inc.

Data Analyst

• Utilize Jupiter within the Advana platform to integrate and utilize naval data across readiness pillars.
• Ability to create reports, dashboards, and data sets for collaboration internally and externally of the organization.
• Ability to bring multiple data sources together, perform data cleanup, shaping, and modeling to produce visualizations and reports that support and enable actionable insights.
• Assist in the collection of data, conduct analytical reviews, and make recommendations for improving Equipment Readiness policies.
• Assist in the promulgation of software development requirements for Readiness Reporting and cost models for NECC and OPNAV on NECC’s behalf.
• Perform analysis of raw data and provide decision-supporting products such as historical trends, reports, graphical data presentations, and recommendations.
• Assist with and/or independently perform quantitative analysis of financial, materiel inventory and materiel assessments data against risk management problem set.
• Utilize mathematical and statistical methods to provide the value of materiel solutions required to increase combat power and readiness from the force down to the individual unit level.
• Assist with and/or independently prepares economic analysis of NECC force wide materiel asset requirements, commonly referred to as Table of Allowance (TOA) and TOA inventory, including conducting research of historical cost data of TOA maintenance, replacement and technical refresh for NECC units (Tables of Allowance).
• Analysis of current resource utilization and projection of future resource requirements for segments (sections) of materiel; and research and completion of routine and/or continuing cost studies.
• Participate in the development of procurement plans and program objectives by evaluating cost/readiness tradeoffs, budget requirements/constraints, etc.
• Assist with and/or independently perform special assignments in keeping with the Materiel Readiness strategic objectives, such as cross-functional team initiatives and force readiness improvement initiatives. Includes administrative assignments to develop knowledge of the mission and organization of the force material readiness community.

Data, Reporting Analyst

AI Engineer

• 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.
• 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.
• 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.

Distinguished AI Software Engineer

• 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.
• Engineer architectures for redundancy and resilience under degraded or disconnected operational conditions.
• Collaborate with cybersecurity and ISSM teams to support ATO and ongoing accreditation automation.
• Integrate emerging technologies, open standards, and modern frameworks into operational systems.
• Communicate complex technical concepts clearly to both executive and engineering stakeholders.

• 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.
• 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.

• 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.

Distinguished AI/ML Engineer

Full-stack Engineer

• Architect and deploy secure, distributed platforms supporting modular, multi-service software environments.
• Design and integrate APIs, microservices, and data pipelines that enable interoperability.
• Define and enforce architectural principles, modularity standards, and software best practices.
• Engineer solutions that bridge cloud, on-prem, and classified enclaves.
• Engineer cloud-native solutions in AWS GovCloud or hybrid IL4–IL6 environments.
• Implement Infrastructure-as-Code (Terraform, CloudFormation, or CDK).
• Build and maintain CI/CD pipelines that embed security and compliance.
• Design or oversee large-scale data ingestion and orchestration environments.
• Transition software and platforms from prototype to production.
• Engineer architectures for redundancy and resilience under degraded conditions.
• Integrate emerging technologies into operational systems.
• Lead by example as a hands-on technical authority and mentor software engineers.

Distinguished AI Software Engineering Lead

• 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.

AI Software Engineering Lead

• Design and implement APIs, data pipelines, and simulation runtime logic
• Develop software using modern programming languages such as Java, Python, C++, or TypeScript/Angular
• Write clean, testable, and maintainable code
• 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
• Implement Infrastructure-as-Code and automation scripts
• Develop distributed systems and data integration frameworks using message buses such as Kafka or Redis
• Build and manage databases (PostgreSQL, MongoDB, graph DBs) and model complex data relationships
• Write, test, and deploy software within secure or classified environments

AI/ML Software Engineer

Machine Learning Engineer

• 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.

AI/ML Engineer

Lead AI/ML Engineer

Distinguished AI/ML Engineering Lead

• 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.