AI Engineer

Reflexive Concepts is seeking an AI Engineer to join our team!

We have an immediate need for an Artificial Intelligence (AI) Engineer. The AI Engineer will work closely with system and software engineers to design, prototype, and integrate AI-enabled capabilities into the existing system architecture. This role centers on applying existing large language models (LLMs) to real mission workflows rather than training new models. The AI Engineer will build agentic solutions — systems of coordinated AI agents and prompts — that automate tasks currently performed manually, streamline authoring and reporting workflows, and enhance the overall effectiveness of intelligence operations.

Responsibilities:

• Agentic Solution Design & Development: Lead the design and development of AI-driven solutions from conception to deployment, building multi-agent workflows in which coordinated agents and prompts work together to produce outputs supporting reporting and dissemination. This includes prototyping, writing production-quality code, and maintaining deployed AI capabilities.
• LLM Integration: Integrate existing foundation models into the system architecture via APIs and orchestration frameworks, selecting the right model and approach for each task rather than building models from scratch.
• Prompt Engineering & Workflow Automation: Design, test, and refine prompts and agent instructions to reliably automate manual tasks, and decompose complex mission workflows into discrete steps an agent pipeline can execute.
• Evaluation & Reliability: Develop robust testing, evaluation, and validation strategies for AI-generated outputs, ensuring accuracy, consistency, and appropriate handling of edge cases before outputs reach analysts and reports.
• Collaboration & Communication: Serve as a key technical liaison, collaborating with cross-functional teams including system engineers, software developers, and domain experts. Effectively present and articulate recommended AI approaches, discussing the tradeoffs and implications of different implementations with both technical and non-technical stakeholders.
• Continuous Improvement: Stay current with the rapidly evolving LLM and agentic AI landscape (new models, orchestration frameworks, and tooling), continuously identifying practical opportunities to apply emerging capabilities to the system.

Required technical skills:

• Strong Python development skills, with experience writing production-quality, maintainable code
• Hands-on experience building applications on top of existing LLMs (e.g., via APIs for commercial or self-hosted models)
• Experience designing multi-agent or multi-step AI workflows, including prompt chaining, tool use, and agent orchestration (e.g., LangChain, LlamaIndex, or custom frameworks)
• Strong prompt engineering skills and experience evaluating and validating LLM outputs
• Experience integrating AI capabilities into existing software architectures (RESTful APIs, microservices)
• Experience with the Amazon Web Services (AWS) cloud computing platform

Desired technical skills:

• Experience with retrieval-augmented generation (RAG) pipelines and vector databases
• Familiarity with LLM observability and evaluation tooling
• Experience conducting exploratory data analysis on structured and unstructured datasets to prepare inputs for AI workflows
• Familiarity with MLOps/LLMOps practices for deploying and monitoring AI capabilities at scale
• Familiarity with domain knowledge surrounding government agency reporting and dissemination policies