AI Software Engineer

About PSE At Pacific Science & Engineering, we design human-centered solutions and intelligent systems that enable humans to partner effectively and safely with leading-edge, high-complexity, and high-consequence military and commercial environments. Our work spans autonomous systems, AI-enabled decision support, advanced data visualization, and mission-critical control systems. Our science-driven approach to human factors engineering, cognitive systems design, and AI integration is unique and industry-leading. We are a multidisciplinary team of cognitive scientists, engineers, data scientists, software developers, and designers working collaboratively to deliver impactful solutions. As an employee-owned company, we offer a flexible work environment, competitive compensation, and incentives tied to performance and impact. If you’re interested in building AI-enabled systems that operate in real-world, high-stakes environments, Pacific Science & Engineering may be the right place for you. Role We are seeking an AI-Enabled Software Engineer / Applied AI Engineer to design, develop, and deploy software systems that integrate machine learning and large language model (LLM) capabilities into operational workflows and user interfaces. This role sits at the intersection of software engineering, machine learning, and human-centered design. You will work closely with cognitive scientists, human factors engineers, and UI/UX designers to build intelligent systems that are not only technically robust, but also usable, interpretable, and aligned with real-world mission needs. We encourage applications from candidates across a range of experience levels, from early-career engineers to experienced practitioners. Responsibilities Design, develop, and deploy AI-enabled applications, with a focus on LLM-integrated systems (e.g., agents, decision-support tools, intelligent interfaces) Build and maintain end-to-end pipelines for machine learning and LLM applications, including data ingestion, preprocessing, model integration, and evaluation Implement retrieval-augmented generation (RAG) systems, including vector database integration and knowledge retrieval strategies Develop and integrate LLM-based capabilities such as: Natural language interfaces Automated content generation and validation Semantic search and question answering Structured information extraction Translate algorithms, models, and system designs into production-quality code Design and implement scalable backend services (APIs, microservices, event-driven systems) to support AI-enabled functionality Develop interactive front-end applications that effectively present AI outputs and support human-AI teaming Conduct model evaluation, validation, and performance optimization, including prompt engineering, fine-tuning, and benchmarking Contribute to DevSecOps and MLOps workflows, including CI/CD pipelines, testing, monitoring, and deployment Collaborate with multidisciplinary teams to ensure AI solutions align with user needs, cognitive constraints, and mission objectives Document system architectures, models, and processes in accordance with industry and DoD standards Stay current with advancements in AI/ML, LLMs, and software engineering practices Desired Knowledge, Skills, Abilities (KSAs) Bachelor’s or advanced degree in Computer Science, Software Engineering, Data Science, Machine Learning, or a related technical field (or equivalent practical experience) Strong programming skills in one or more languages such as Python, JavaScript/TypeScript, Java, or C++ Experience developing software applications that integrate machine learning or AI capabilities Familiarity with LLMs and modern AI tooling, including APIs and/or local model deployment Experience building backend services and APIs (REST, GraphQL, or WebSocket-based systems) Ability to design and implement scalable, modular software architectures Strong analytical and problem-solving skills; ability to quickly understand complex systems and requirements Ability to work effectively in multidisciplinary teams Excellent written and verbal communication skills Ability to obtain and maintain a US DoD Security Clearance (US Citizenship required) Advanced KSAs Experience developing LLM-powered applications, including: Retrieval-Augmented Generation (RAG) Vector databases (e.g., Pinecone, FAISS, Redis, or equivalent) Prompt engineering and evaluation frameworks Fine-tuning methods (e.g., LoRA or parameter-efficient tuning) Experience with Graph-RAG or knowledge graph integration Experience deploying AI systems in secure, edge, or resource-constrained environments Familiarity with cloud platforms (e.g., Azure, AWS) and AI services (e.g., Azure OpenAI, Cognitive Services) Experience with containerization and orchestration (Docker, Kubernetes) Experience with MLOps/DevSecOps practices, including CI/CD pipelines and automated testing Experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn Experience with full-stack development, including modern front-end frameworks (e.g., React, Node.js, TypeScript) Experience integrating user analytics, telemetry, and feedback loops into applications Ability to collaborate effectively with UI/UX designers and human factors engineers to translate designs into functional systems Knowledge of human-AI interaction, explainability, or decision-support system design Familiarity with DoD systems, environments, and compliance requirements Experience leading technical efforts, conducting code reviews, and mentoring team members Experience contributing to proposals, technical reports, or publications in AI/ML or related fields Active SECRET level or higher DoD Security Clearance