Data Science & AI/PQC Engineer — Federal Mission Solutions

Description

Position Overview

Diaconia is seeking a mid-level Data Science & AI/PQC Engineer to design and deliver AI-enabled cybersecurity and post-quantum cryptography (PQC) capabilities for federal mission customers. This role blends applied machine learning, data engineering, cloud-native software delivery, and cryptographic modernization to help agencies identify cryptographic assets, score quantum and cyber risk, monitor compliance, and transition legacy environments toward quantum-resilient architectures. The engineer will contribute to mission-facing prototypes, secure deployments, technical documentation, and stakeholder demonstrations in support of federal cyber modernization efforts.

Key Responsibilities

• Develop AI-driven PQC readiness capabilities that support cryptographic asset inventory, key-management mapping, legacy-system dependency analysis, automated risk scoring, and compliance monitoring for federal networks
• Integrate cybersecurity and infrastructure data from network scans, SIEM/security telemetry, vulnerability tools, configuration repositories, cryptographic discovery outputs, and mission systems into analytics-ready datasets
• Engineer cloud-native prototypes using Python, APIs, Docker, Kubernetes/Helm, CI/CD, and AWS or Azure government cloud environments to move analytics from proof-of-concept into secure, repeatable deployments
• Evaluate AI/ML effectiveness using mission-relevant metrics such as detection accuracy, false-positive rates, coverage, latency, response time, model drift, and remediation prioritization value
• Apply AI/ML techniques to structured and unstructured federal datasets, including network telemetry, vulnerability findings, cryptographic inventories, logs, NLP, time-series forecasting, anomaly detection, and classification models
• Develop and iterate on data pipelines to ingest, clean, transform, and analyze large-scale government datasets, such as network logs, cryptographic asset inventories, vulnerability scans, procurement data, case management records, sensor      feeds, and supply chain data
• Prototype and evaluate large language model (LLM) applications including retrieval-augmented generation (RAG), prompt engineering, agentic workflows, and analyst-assist capabilities tailored to cyber, compliance, and mission assurance use cases
• Translate mission requirements from federal agency stakeholders into technical problem statements, data-driven solution approaches, backlog items, model evaluation plans, and implementation roadmaps
• Build dashboards and data visualizations to communicate threat trends, cryptographic risk, migration priority, model performance, compliance status, and analytical findings to both technical and non-technical government audiences
• Support responsible AI practices by contributing to model documentation, test plans, explainability artifacts, bias and performance assessments, and governance workflows aligned to applicable federal AI guidance (e.g., OMB M-25-21, OMB M-25-22, EO 14179, NIST AI RMF)
• Collaborate in agile teams by participating in sprint planning, demos, retrospectives, code reviews, experiment reviews, and technical documentation for secure federal delivery
• Present findings to internal teams and, where appropriate, to federal agency stakeholders through demos, briefings, white papers, remediation roadmaps, and architecture tradeoff discussions

Disclaimer "The responsibilities and duties outlined in this job description are intended to describe the general nature and level of work performed by employees within this role. However, they are not exhaustive and may be subject to change or modification at any time to meet the evolving needs of the organization.  

Requirements

 Required Qualifications

• 3+ years of professional experience in data science, machine learning engineering, software engineering, cybersecurity analytics, cryptography modernization, or related applied technology delivery
• Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Cybersecurity, Information Systems, or a related technical field; additional relevant experience may substitute for degree requirements
• Proficiency with Python and SQL and experience building data pipelines, analytical workflows, APIs, dashboards, or production-grade AI/ML applications
• Working knowledge of cybersecurity and cryptographic concepts such as TLS, PKI, key management, encryption algorithms, vulnerabil