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About This Role
Overview
Pyramid Systems is seeking a Senior AI Software Developer to build AI\-enabled applications and lead the modernization of legacy federal systems including COBOL\-to\-Python re\-platforming—for our HUD customer and the AIR product platform. This is an applied AI engineering role focused on delivering production software that leverages modern AI services (LLMs, APIs, and cloud AI platforms), not building foundation models. You will work directly with HUD stakeholders and the NEXUS/AIR team to ship secure, compliant, mission\-aligned capabilities.
Responsibilities
- Serve as a senior technical leader on AI\-enabled application work — own end\-to\-end architecture, set technical direction across multiple projects, and represent Pyramid Systems in technical conversations with HUD and other federal stakeholders.
- Design, develop, and deploy AI\-enabled applications that integrate LLMs, generative AI services, and cloud AI APIs into production federal systems
- Build full\-stack web applications end to end — modern front\-end interfaces (React, Angular, or Vue with TypeScript), back\-end services (Python with Django/Flask/FastAPI or Node.js), and the database, API, and integration layers behind them
- Design and implement secure CI/CD pipelines and DevSecOps practices — automated build, test, and deploy; SAST/DAST and dependency scanning; container and image scanning; secrets management; SBOM generation; and shift\-left security gates
- Author infrastructure as code (Terraform, CloudFormation, or equivalent) and manage containerized workloads (Docker, Kubernetes) on FedRAMP\-authorized cloud environments
- Implement logging, monitoring, tracing, and alerting (CloudWatch, Datadog, OpenTelemetry, or similar) so AI\-enabled applications are observable and operable in production
- Drive engineering quality through automated testing (unit, integration, end\-to\-end), Git\-based code review, and trunk\-based or GitHub\-flow branching practices
- Support Agile delivery (Scrum or Kanban) including sprint planning, backlog refinement, and demos with federal product owners
- Lead modernization of legacy federal applications, including translating and re\-platforming COBOL (and other legacy languages) to Python and modern cloud\-native architectures
- Partner with HUD stakeholders, product, and engineering teams to translate mission needs into secure, compliant, AI\-enabled software solutions
- Build and maintain APIs, microservices, and integration layers that embed AI capabilities into customer\-facing and back\-office applications
- Apply the NIST AI Risk Management Framework (AI RMF 1\.0\) and related federal guidance (e.g., OMB M\-24\-10, EO 14110\) to ensure trustworthy, responsible AI use
- Implement responsible\-AI practices including evaluation, monitoring, prompt and output safety, bias mitigation, explainability, and human\-in\-the\-loop controls
- Ensure compliance with federal security and privacy requirements (FISMA, FedRAMP, NIST 800\-53, Section 508\) across all AI\-enabled deliverables
- Contribute to the AIR platform (including AIR\-Quire and AIR Grant) by building demos, prototypes, and reusable components that accelerate client engagements
- Mentor and coach mid\-level developers, lead code reviews, and drive engineering best practices for AI\-enabled application development
- Support proposal efforts and client demonstrations as named or contributing technical personnel, including for the HUD recompete (TO1, TO2, FCAT)
- Research and evaluate emerging AI tools, frameworks, and patterns (RAG, agentic workflows, model orchestration) and pilot their use in federal contexts
- Work with legacy teams to identify and leverage appropriate AI tools and implementations to increase developer velocity, improve quality, and modernize legacy systems.
- Support culture changes surrounding AI use at Federal Agencies including government stakeholders and internal development teams.
- Help drive organizational and cultural adoption of AI capabilities across project teams by addressing concerns, increasing understanding, and demonstrating practical mission\-focused value.
- Collaborate with leadership to identify modernization opportunities that improve operational efficiency, reduce technical debt, and support Agency mission objectives.
- Communicate AI modernization strategies and benefits to technical and non\-technical stakeholders, including articulating how AI can enhance mission delivery, improve workforce efficiency, and augment existing teams.
Qualifications
- US Citizenship is required
- Bachelor's or master's degree in Computer Science, Software Engineering, or a related field
- 8\+ years of software development experience, with at least 4\+ years building AI\-enabled applications (LLM integration, generative AI, or applied AI/ML in production)
- Strong programming skills in Python; experience with at least one legacy language (COBOL, mainframe languages, or similar) and a demonstrated ability to modernize legacy code
- Hands\-on experience integrating commercial and open\-source AI services and APIs (e.g., OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Vertex AI)
- Hands\-on experience with cloud platforms (Azure, AWS, or Google Cloud), including deploying production applications
- Working knowledge of the NIST AI Risk Management Framework (AI RMF 1\.0\) and federal AI policy (OMB M\-24\-10, EO 14110\)
- Experience with REST APIs, microservices architecture, and modern application design patterns
- Strong understanding of secure software development practices in federal environments (FISMA, FedRAMP, NIST 800\-53\)
- Demonstrated experience as a technical lead, principal engineer, or senior architect — including leading small teams, owning architecture for production systems, and mentoring other engineers
- Excellent communication skills and ability to engage directly with federal customers
Preferred Qualifications
- Experience with generative AI, large language models (LLMs), and NLP applications
- Familiarity with MLOps tools (e.g., MLflow, Kubeflow, Airflow)
- Knowledge of containerization tools (Docker, Kubernetes)
- Experience working with big data technologies (e.g., Spark, Hadoop)
- PhD or advanced specialization in AI/ML is a plus
Target Pay RangeThe 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 MinUSD $125,941\.00/Yr.
Pyramid MaxUSD $157,000\.00/Yr.
Pay: $124,249\.21 \- $149,633\.45 per year
Benefits:
- 401(k)
- Dental insurance
- Flexible schedule
Application Question(s):
- Do you have 8\+ years of software development experience, with at least 4\+ years building AI\-enabled applications (LLM integration, generative AI, or applied AI/ML in production) ?
Education:
- Bachelor's (Required)
Work Location: Remote
Salary Context
This $124K-$149K range is in the lower quartile 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 ($136K) sits 23% below the category median. Disclosed range: $124K to $149K.
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.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
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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