Data Science & AI/PQC Engineer — Federal Mission Solutions

Diaconia LLC • Gaithersburg, MD

4d • $150,000 - $185,000

About The Position

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.

Requirements

• 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, vulnerability assessment, secure communications, and risk remediation
• Experience with cloud or containerized delivery using tools such as AWS, Azure, Docker, Kubernetes, Git, CI/CD pipelines, and Linux-based development environments
• U.S. citizenship and ability to obtain and maintain a U.S. government security clearance; active Secret, Top Secret, or TS/SCI clearance may be required by program
• Strong analytical thinking and ability to frame ambiguous problems into tractable analytical approaches
• Excellent written and verbal communication skills; ability to explain technical concepts to non-technical stakeholders

Nice To Haves

• Hands-on experience with post-quantum cryptography, crypto-agility, cryptographic discovery, PQC migration planning, or implementation of NIST PQC standards such as ML-KEM, ML-DSA, and SLH-DSA
• Experience building AI-enabled cybersecurity capabilities, including threat detection, anomaly detection, automated risk scoring, compliance monitoring, SIEM/log analytics, analyst-assist workflows, or cyber operations automation
• Experience deploying AI/ML or software capabilities into secure federal environments, such as DoD, IC, CUI, FedRAMP, CMMC, RMF, Zero Trust, CAC-enabled, air-gapped, or otherwise constrained mission settings
• Familiarity with secure communications and infrastructure modernization, including PKI, identity systems, key management, cloud security, encryption modernization, and legacy-system interoperability
• Experience with deep learning frameworks (PyTorch, TensorFlow, Hugging Face Transformers) and classical ML libraries (scikit-learn, XGBoost, pandas) used in applied analytics delivery
• Hands-on exposure to LLMs and generative AI applications including prompt engineering, fine-tuning, RAG pipelines, vector stores, model evaluation, and agentic frameworks such as LangChain, LangGraph, Semantic Kernel, or AutoGen
• Familiarity with cloud platforms (AWS GovCloud, Azure Government, or Google Cloud) and MLOps tooling such as MLflow, SageMaker, Vertex AI, Airflow, Kubeflow, or Databricks workflows
• Experience with data visualization tools (Tableau, Power BI, Plotly Dash, Kibana, Grafana, or similar) for executive dashboards, analyst workflows, and operational monitoring
• Knowledge of federal or mission data sources including agency-specific systems, network/security telemetry, vulnerability management platforms, USASpending, Data.gov, Census Bureau APIs, or operational mission repositories
• Prior professional, research, or project experience in a government, defense, intelligence, cybersecurity, public sector, or regulated commercial environment
• Coursework, projects, or applied experience in AI governance, responsible AI, trustworthy AI, model risk management, privacy, cybersecurity policy, or federal technology acquisition

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

Benefits

• Quantum-resilient mission modernization: Build AI-enabled capabilities that help federal agencies understand cryptographic exposure, prioritize PQC migration, and improve mission assurance against emerging quantum-enabled cyber threats
• End-to-end technical ownership: Contribute across prototype design, data ingestion, ML experimentation, cloud deployment, stakeholder demonstrations, and transition planning for operational environments
• Mission-driven impact: Your work will directly support federal agencies tackling challenges in AI-driven cybersecurity, PQC readiness, cryptographic compliance, mission assurance, supply chain resilience, fraud detection, workforce analytics, and more
• Technical depth: Hands-on experience applying AI/ML, LLM, MLOps, cloud engineering, data engineering, and PQC methods to complex, real-world federal datasets - not toy problems
• Federal domain expertise: Exposure to the federal acquisition, compliance, cyber modernization, SBIR transition, and program environment that shapes how AI and PQC capabilities are deployed in government
• Mentorship: Work with senior data scientists, ML engineers, cybersecurity architects, and cryptography specialists who provide technical guidance and career coaching throughout the role
• Professional development: Access to internal learning resources, technical communities, industry certifications (AWS, Azure, Google Cloud, security, data, and AI), and speaker series

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level