AI Systems Engineer

Command Post Technologies, Inc.•Orlando, FL

About The Position

The AI Systems Engineer is responsible for designing, building, and delivering practical artificial intelligence and machine learning solutions that support internal research, development, and business applications. This position combines hands-on software development, deep learning, and systems architecture to translate customer needs and emerging ideas into clear technical requirements, reliable solution designs, and working products. The role will support the development and deployment of advanced AI capabilities, including agentic AI systems operating in secure, air-gapped, on-premises, and edge environments. The ideal candidate is a curious, self-directed technical builder who takes initiative, enjoys solving complex problems, and can independently drive work from concept through implementation. This individual will own key technical efforts, mentor junior engineers, collaborate with technology leaders, and help strengthen the organization’s broader AI capabilities.

Requirements

Bachelor's degree in Systems Engineering, Computer Science, Software Engineering, Data Science, Applied Mathematics, or a related field, or equivalent practical experience demonstrated through delivered software and AI solutions.
At least 5 years of professional development experience, including at least 3 years building, integrating, or deploying machine learning or LLM-based capabilities.
Strong Python proficiency, including maintainable, tested code and integration of ML/LLM capabilities into larger applications.
Hands-on experience with PyTorch or a comparable framework for training, fine-tuning, or evaluating models.
Solid ML fundamentals, including data preparation, evaluation, and generalization, and a practical understanding of transformer models, including embeddings, tokenization, attention, and context limits.
Experience deploying models or LLM services behind APIs, with working knowledge of latency, throughput, and GPU resource tradeoffs.
Hands-on experience building LLM-based workflows or agents, including tool integration, RAG, context and state management, structured outputs, and retries and failure recovery.
Ability to design repeatable evaluations of model and agent behavior, including baselines, meaningful metrics, and diagnosis of failure modes.
Working knowledge of AI application security, including least-privilege tool access, secrets management, audit logging, and prompt-injection and data-exfiltration risks.
Experience designing software components and interfaces, making sound technical tradeoffs, and judging when a simpler non-AI approach is sufficient.
Experience with version control, code review, testing, and tracking tools such as Jira and Confluence.
U.S. Citizen with the ability to obtain and maintain a Secret security clearance.

Nice To Haves

Active Secret security clearance.
Experience developing AI solutions for Department of Defense or Federal Government customers, including SBIR/STTR proposals, technical capture, applied research, or transitioning prototypes into operational capabilities.
Experience with AI platforms and agent frameworks such as Azure AI Foundry, Microsoft Copilot Studio, OpenAI, Anthropic Claude, LangGraph, LangChain, Semantic Kernel, CrewAI, or AutoGen, including multi-agent orchestration.
Cybersecurity and secure development experience, including DevSecOps practices such as CI/CD, automated security testing, containerization, or infrastructure as code.
Experience deploying AI systems in air-gapped, on-premises, or resource-constrained edge environments, including managing offline dependencies and model artifacts and configuring GPU workstations, servers, or edge hardware.
Familiarity with the NIST AI Risk Management Framework or comparable AI risk and assurance practices.
Experience with model optimization, such as fine-tuning, distillation, quantization, or multi-GPU and distributed inference.
Experience with MLOps practices such as experiment tracking, data and model versioning, deployment, and production monitoring.
Experience with Rust or another systems programming language.

Responsibilities

Design, develop, test, and deploy AI-enabled applications and solutions.
Create and implement AI systems that leverage LLMs, autonomous workflows, tool integration, and agentic solutions.
Develop AI-driven automation solutions to improve operational efficiency and decision-making.
Build and integrate AI agents using commercial and open-source AI platforms and frameworks.
Collaborate with engineers, data scientists, developers, and business stakeholders to identify AI use cases and deliver effective solutions.
Evaluate emerging AI technologies and recommend innovative approaches to customer challenges.
Develop technical documentation, demonstrations, prototypes, and proof-of-concept solutions.
Support customer engagements, technical presentations, and solution demonstrations.