Senior Python Developer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Python Developer based in the United States.

Take on a senior engineering role at the intersection of cybersecurity, Python development, and artificial intelligence. You’ll help advance a security compliance platform designed to support complex government and defense environments. Combining scalable security automation with production-ready AI capabilities, you’ll build solutions that improve how cybersecurity requirements are processed, validated, and operationalized. You’ll work with both locally hosted and cloud-based AI models, developing reliable systems for connected, restricted, and air-gapped environments. This remote opportunity offers significant technical ownership, architectural influence, and the chance to mentor developers while delivering secure, mission-critical software.

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…