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About This Role
Overview:
The Senior Principal Software Engineer/Developer – AI serves as a senior, hands\-on full\-stack AI engineer and technical authority, leading the technical strategy, design, and delivery of large\-scale mission\-critical AI systems supporting federal programs (e.g., HUD, AIR platform). This role combines senior technical leadership, hands\-on expertise in Python\-based AI/ML systems (including large language models), and ownership of enterprise architecture, governance, and innovation.
Responsibilities:
- Serve as the primary technical authority, defining AI and application architecture across multiple programs
- Establish enterprise modernization roadmaps aligned to mission outcomes, compliance, and scalability
- Lead architecture for distributed, cloud\-native, and hybrid AI systems
- Define and enforce reference architectures, standards, and reusable frameworks
- Drive cross\-program technical decision\-making to ensure interoperability, security, and long\-term sustainability
- Advise senior federal stakeholders (SES\-level and above) on AI adoption, modernization, and risk management
- Lead design, development, and deployment of advanced AI solutions using Python as the primary development language, including large language models (LLMs) and foundation models, Retrieval\-Augmented Generation (RAG) systems, agentic workflows, and orchestration frameworks
- Architect and implement scalable ML systems and services built on Python\-based frameworks and APIs
- Build full\-stack AI applications end to end, from user\-facing interfaces to back\-end services, APIs, and data layers
- Integrate AI and LLM capabilities into existing enterprise applications and legacy platforms (e.g., content management, case management, and records systems) via APIs, middleware, and event\-driven patterns
- Define and implement distributed training strategies (GPU/TPU clusters, parallelization, optimization)
- Oversee full ML lifecycle in partnership with the Senior Data Scientist: data pipelines, feature engineering, training, evaluation, deployment, and monitoring
- Drive model optimization techniques (quantization, distillation, caching) to improve performance and cost
- Establish robust MLOps practices leveraging Python\-driven automation, pipelines, and tooling
- Stand up the enterprise CI/CD\-to\-AI/MLOps pipeline, beginning with time\-boxed proofs of concept and MVP implementations that mature into production systems
- Serve as subject matter expert in federal AI policy (e.g., NIST AI RMF, OMB M\-25\-21 and M\-25\-22, Executive Order 14179\)
- Define and operationalize Responsible AI frameworks, including model validation and evaluation, bias mitigation and fairness, and explainability, auditability, and safety
- Ensure compliance with FISMA, FedRAMP, NIST 800\-53, privacy, and Section 508 requirements
- Lead large\-scale modernization initiatives (e.g., legacy\-to\-cloud, microservices transformation, including Python\-based refactoring and re\-platforming efforts)
- Define repeatable modernization frameworks and accelerators
- Oversee DevSecOps pipelines, CI/CD automation, zero\-trust architectures, and secure software supply chain practices
- Ensure delivery of resilient, high\-availability systems in regulated federal environments
- Lead multiple concurrent engineering efforts across integrated teams
- Provide technical leadership to architects, engineers, and DevSecOps specialists, including establishing Python coding standards and engineering best practices
- Mentor senior engineers and technical leaders; elevate engineering excellence and code quality
- Support technical strategy in proposals, captures, and client engagements
- Contribute to thought leadership (whitepapers, architecture patterns, platform strategy)
- Expert\-level proficiency in Python, including building large\-scale AI/ML systems, APIs, and data pipelines
- Full\-stack engineering skills, including front\-end frameworks, back\-end services, RESTful APIs, microservices, and cloud\-native deployment (e.g., containers, Kubernetes)
- Deep expertise in machine learning and deep learning, particularly transformer\-based models and LLMs
- Hands\-on experience with ML frameworks (PyTorch, TensorFlow, JAX) and distributed training (DeepSpeed, FSDP, Horovod)
- Proven ability to integrate AI capabilities into existing and legacy enterprise systems (e.g., legacy CMS or COTS platforms) using APIs, middleware, connectors, and event\-driven architectures
- Strong understanding of large\-scale data systems and ML evaluation methodologies
- Experience working with sensitive data, including PII safeguards such as anonymization, masking, and data loss prevention
- Experience with enterprise integration technologies, including REST/SOAP services, message queues, ETL pipelines, and SQL/NoSQL databases
- Expertise designing AI systems in cloud\-native, distributed environments across AWS, Azure, and GCP
- Proficiency with managed generative AI services (e.g., AWS Bedrock, Azure OpenAI Service, Google Vertex AI) and integrating frontier models such as GPT, Claude, and Gemini
- Hands\-on experience with LLM application stacks, including orchestration frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel), embeddings, vector databases, and prompt engineering
- Executive communication skills with experience influencing senior leaders
- Demonstrated ability to own solutions end to end — from discovery and prototyping through production deployment, integration, and ongoing support
- Ability to balance strategic vision with deep hands\-on technical execution
Qualifications:
- US. Citizenship required
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
- 12–15\+ years of software engineering experience, including significant leadership responsibility
- 8\+ years of applied AI/ML experience, including building and deploying production systems (LLMs, generative AI, and large\-scale or distributed model systems) Expert\-level Python development experience, including designing production\-grade ML systems, data pipelines, and microservices\-based architectures
- Deep experience with cloud platforms (Azure, AWS, GCP), including FedRAMP environments
- Experience with AI platforms and architectures (e.g., AWS Bedrock, Azure OpenAI Service, Google Vertex AI, RAG, agents)
- Proven success delivering enterprise\-scale systems and modernization programs
- Strong background in microservices, APIs, distributed systems, and DevSecOps practices
- Experience managing GPU\-based infrastructure or high\-performance ML environments
- Demonstrated ability to translate AI research into production system
- Active clearance (Public Trust, Secret, or higher) preferred
- Experience with HUD or federal civilian agencies preferred
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 below the median for AI Software Engineer roles in our dataset (median: $190K across 219 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Pyramid Systems Inc, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($188K) sits 19% below the category median. Disclosed range: $166K to $210K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Pyramid Systems Inc AI Hiring
Pyramid Systems Inc has 4 open AI roles right now. They're hiring across AI Software Engineer, Data Scientist, AI/ML Engineer. Based in US. Compensation range: $157K - $210K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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