Senior Python Developer

Accountabilities • Design, develop, test, and optimize Python-based scanning capabilities that perform reliably across large, segmented, and federated network environments, including restricted government settings.

• Build and integrate production AI capabilities using local, self-hosted models and cloud-hosted AI services, selecting appropriate architectures based on security, connectivity, accuracy, performance, and cost.

• Develop AI-assisted workflows for ingesting, interpreting, structuring, and validating new and updated DISA Security Technical Implementation Guides (STIGs), including the management of organizationally defined parameters and cybersecurity content mappings.

• Automate complex cybersecurity and product workflows through intelligent document processing, structured data extraction, classification, transformation, validation, exception handling, and human-in-the-loop review.

• Establish measurable outcomes for AI-driven automation, including accuracy, throughput, quality, and processing time.

• Integrate and orchestrate AI models through maintainable abstractions that support different mission environments and technical requirements.

• Contribute to the AI/ML lifecycle, including dataset preparation, model selection, evaluation, prompt engineering, retrieval design, fine-tuning and other adaptation techniques, inference optimization, validation, and production monitoring.

• Expand cybersecurity compliance coverage beyond operating systems to include applications, databases, application servers, middleware, and other software layers.

• Own development initiatives from planning through deployment and stabilization, resolving customer findings and communicating progress, risks, dependencies, and technical trade-offs to product leadership.

• Maintain high engineering standards through architecture contributions, code reviews, automated testing, documentation, observability, secure development practices, and production support.

• Provide technical leadership by exercising independent judgment, influencing architecture and prioritization decisions, mentoring developers, and improving engineering practices.

• Collaborate with architecture, dashboard, cybersecurity content, product, and customer-facing teams to align scanner outputs, AI-generated and ingested content, validation workflows, and reporting.

Requirements

• Bachelor’s degree in computer science, software engineering, data science, AI/ML, or a related field. Relevant professional experience may be considered in place of a degree.

• At least 8 years of relevant professional experience, including a minimum of 4 years of professional Python software development.

• Demonstrated experience designing, developing, testing, deploying, and operating production Python software and AI-enabled applications.

• Hands-on experience integrating local or self-hosted AI/large language models (LLMs), cloud-hosted AI services, or model APIs.

• Practical knowledge of AI/ML techniques, including data preparation, model training concepts, model selection, validation, evaluation, inference, and model adaptation.

• Experience applying AI to automate complex business, engineering, or analytical workflows with measurable operational impact.

• Experience developing distributed data-collection systems or comparable software, with an understanding of performance, reliability, concurrency, and large-scale deployment across segmented or federated networks.

• Strong knowledge of software engineering principles, network architectures, cybersecurity technologies, and regulatory or compliance requirements.

• Understanding of prompting, retrieval-augmented generation (RAG), fine-tuning, and other approaches for determining whether AI models are suitable for production use cases.

• Familiarity with workflow automation, intelligent document processing, data extraction, validation methodologies, and human-in-the-loop review.

• Ability to evaluate the trade-offs between local inference and cloud models, including security boundaries, latency, cost, model capabilities, and disconnected deployment environments.

• Strong analytical and problem-solving skills, sound technical judgment, and the ability to analyze existing codebases and make architectural decisions independently.

• Effective communication, organizational, and project management skills, with the ability to manage competing priorities and meet deadlines.

• Ability to work collaboratively with technical teams, leadership, customers, and external stakeholders while handling confidential information appropriately.

• U.S. citizenship and the ability to pass pre-employment background screening and obtain and maintain a security clearance.

• Willingness and ability to travel up to 10%.

Preferred qualifications:

• Active Secret or Top Secret security clearance.

• Experience with DISA STIGs, SCAP content, or comparable cybersecurity compliance frameworks.

• Experience developing or deploying software in Department of Defense or other federal environments, particularly restricted, air-gapped, disconnected, or accreditation-controlled networks.

• Familiarity with RAG, embeddings, vector databases, structured outputs, tool or function calling, document parsing, information extraction, agentic workflows, and AI evaluation frameworks.

• Experience deploying and optimizing local or self-hosted models in restricted or resource-constrained environments, including model serving, quantization, and GPU/CPU performance trade-offs.

• Hands-on experience with cloud AI platforms and hosted model services, such as Azure OpenAI, AWS Bedrock, OpenAI APIs, or Google Vertex AI.

• Experience with model and version management, evaluation pipelines, experiment tracking, monitoring, quality controls, and production governance for AI-enabled software.

• Experience leading development teams through task assignment, architecture and code reviews, mentoring, and stakeholder coordination.

• Experience contributing to enterprise dashboards and full-stack web applications, including reporting, visualization, usability, workflow and evidence management, role-based access, and performance optimization.

• Familiarity with RMF, CORA, and Department of War cybersecurity assessment and authorization processes.

• Experience supporting commercial or government-off-the-shelf (GOTS) products through government evaluation, accreditation, authorization, or Approved Products List (APL) processes.

• Production experience using Go for endpoint agents, scanners, network services, security tooling, or distributed systems.

Benefits

• Employment type: Full-time.

• Work arrangement: Fully remote within the United States.

• Paid time off: Paid holidays and paid time off.

• Retirement savings: 401(k) plan with employer matching.

• Healthcare: Health insurance plans.

• Dental and vision: Dental and vision coverage.

• Additional insurance: Life and disability benefits.

• Technical challenge and ownership: Opportunity to work on production AI, cybersecurity automation, and software supporting complex federal and defense environments.

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