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About This Role
Overview:
The Senior Principal Software Engineer/Developer will serve as a lead enterprise architect and AI engineering authority, responsible for defining and delivering large\-scale, mission\-critical AI\-enabled systems for HUD and the AIR platform. This role goes beyond hands\-on development to include enterprise\-wide technical strategy, cross\-program architecture leadership, executive\-level stakeholder engagement, and AI governance and innovation leadership in federal environments.
Responsibilities:
- Serve as the primary technical authority across multiple programs, defining AI and application architecture strategy at the enterprise level
- Establish long\-term modernization roadmaps for federal systems, aligning AI adoption with mission outcomes and compliance requirements
- Act as a trusted advisor to senior federal stakeholders (SES\-level and above) on AI strategy, system modernization, and technology risk
- Drive technical decision\-making across portfolios, ensuring scalability, interoperability, and long\-term sustainability
- Own end\-to\-end enterprise architecture across multiple interconnected systems, platforms, and programs
- Lead design of complex, distributed AI\-enabled architectures, including multi\-cloud and hybrid environments
- Define and enforce architecture standards, reference architectures, and reusable frameworks across teams
- Lead development of advanced AI solutions, including RAG systems at scale, agentic workflows and orchestration frameworks, and AI\-driven automation across mission workflows
- Establish AI engineering best practices and standards across the organization
- Evaluate and lead adoption of emerging AI technologies, guiding pilots into production\-ready capabilities
- Drive innovation initiatives tied to AIR platform growth and differentiation
- Oversee multiple concurrent development efforts, ensuring alignment with architecture, security, and delivery goals
- Provide technical leadership across integrated product teams, including architects, engineers, and DevSecOps specialists
- Ensure delivery of high\-availability, production\-grade systems supporting federal missions
- Serve as a subject matter expert in federal AI policy and governance, including NIST AI RMF, OMB M\-24\-10, and EO 14110
- Establish and oversee organization\-wide responsible AI frameworks, including model evaluation and validation, bias mitigation strategies, and explainability and auditability
- Ensure full compliance across programs with FISMA, FedRAMP, NIST 800\-53, privacy, accessibility (Section 508\), and security mandates
- Lead large\-scale legacy modernization initiatives, including enterprise COBOL\-to\-Python transformations, migration to cloud\-native, and microservices\-based architectures
- Define repeatable modernization frameworks and accelerators
- Oversee implementation of enterprise\-grade DevSecOps pipelines and platform engineering practices
- Drive adoption of, advanced CI/CD automation, Zero\-trust architectures, and secure supply chain practices (SBOM, scanning, policy enforcement)
- Mentor senior engineers, architects, and technical leads
- Build and scale high\-performing engineering teams
- Lead technical hiring, workforce planning, and capability development
- Play a key leadership role in proposals, captures, and recompetes
- Serve as technical lead in client presentations, demos, and strategy sessions
- Contribute to thought leadership (whitepapers, architecture frameworks, innovation roadmaps)
Qualifications:
Knowledge/Skills/Abilities: Initial:* Proven ability to operate as an Enterprise architect, chief engineer, or technical director\-level leader
- Strong executive communication skills with demonstrated success influencing C\-suite or federal senior leadership
- Deep expertise in scaling engineering organizations and delivering across complex programs
- Ability to balance strategic vision with hands\-on technical depth
Qualifications:
- US Citizenship is required
- Bachelor's or master's degree in Computer Science, Software Engineering, or a related field
- 12–15\+ years of software engineering experience, including 6–8\+ years in AI\-enabled systems (LLMs, generative AI, applied ML in production) *(enhanced from 8\+ years total)*
- Demonstrated experience leading enterprise\-scale architecture and multi\-team delivery efforts
- Expert\-level proficiency in Python and modern backend frameworks and Full\-stack architectures and modern front\-end technologies
- Deep experience in Cloud\-native platforms (Azure, AWS, GCP) at scale and Multi\-environment deployments in FedRAMP\-authorized environments
- Extensive experience with AI/ML platforms (OpenAI, Bedrock, Vertex AI, etc.) and Advanced AI architectures (RAG, orchestration, agents)
- Proven track record of leading large modernization programs (COBOL\-to\-modern stack transformation) and delivering mission\-critical federal systems
- Deep knowledge of NIST AI RMF and federal AI governance frameworks and secure software development in federal environments
- Strong experience with Microservices, APIs, distributed systems design and DevSecOps and platform engineering leadership
- Prior experience serving as Lead Architect, Chief Engineer, or Technical Director on federal programs
- Active clearance (Public Trust, Secret, or higher strongly preferred)
- Prior HUD or federal civilian agency leadership experience
- Experience contributing to organizational AI strategy or product platforms (e.g., AIR\-like platforms)
Target Pay Range: The below listed pay range for this position is not a guarantee of compensation or salary. The final offered salary will be influenced by a host of factors including, but not limited to, geographic location, Federal Government contract labor categories and contract wage rates, relevant prior work experience, specific skills and competencies, education, and certifications. Our employees value the flexibility at Pyramid Systems that allows them to balance quality work and their personal lives. We offer competitive compensation, benefits, to include our Employee Stock Ownership Program, FlexPTO, and learning and development opportunities. Pyramid Min: USD $166,091\.00/Yr. Pyramid Max: USD $210,000\.00/Yr. Why Pyramid?: Pyramid Systems, Inc. is an award\-winning, technology leader, driving digital transformation across federal agencies. We empower forward\-thinking innovations, accelerate production\-ready software, and deliver secure solutions so federal agencies can meet their mission goals. Voted a Top Workplace, both regionally (Washington, DC) and Nationally (USA) the past 2 years (2023 and 2024\) based on the feedback from our employees, we are headquartered in Fairfax, VA. and have a growing national footprint. We value and promote our Flexible Workplace approach because of the positive impacts it has on work\-life integration. We remain committed to ensuring every employee’s voice is heard, performance and results are recognized and rewarded, development and advancement is a focus, and diversity, equity and inclusion is a company priority. We offer competitive compensation and benefits (including a recently launched Employee Stock Ownership Plan \- ESOP), a robust performance\-based rewards program, and we know how to have fun! Our people and culture have endured and delivered for our clients for nearly three decades. EEO Statement: Pyramid Systems, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.
Salary Context
This $166K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Pyramid Systems Inc, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($188K) sits 5% above the category median. Disclosed range: $166K to $210K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Pyramid Systems Inc AI Hiring
Pyramid Systems Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span US, Remote, US. Compensation range: $149K - $210K.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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