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About This Role
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, 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
Preferred Qualifications
- 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
What You'll Gain
- 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
Salary Context
This $150K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Diaconia, LLC., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($167K) sits 23% below the category median. Disclosed range: $150K to $185K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Diaconia, LLC. AI Hiring
Diaconia, LLC. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Gaithersburg, MD, US. Compensation range: $185K - $185K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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