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About This Role
Lead Software Architect – AI Operating Systems \& Enterprise AutomationAbout One Algorithm
One Algorithm is a certified EDWOSB technology company building a proprietary AI\-powered operating system designed to automate and integrate the core functions of a modern federal contractor and technology organization.
Our platform will serve as the operational backbone of the business, managing opportunity identification, Go/No\-Go decision support, capture management, proposal development, compliance operations, contract administration, partner lifecycle management, project delivery, financial operations, organizational knowledge management, and executive decision support.
A key component of this platform is the development of a secure, voice\-first AI operating environment capable of interacting with employees, leadership, partners, and enterprise systems through natural language.
The platform combines conversational AI, enterprise knowledge retrieval, organizational memory, workflow orchestration, business intelligence, and operational automation into a unified operating environment.
Our long\-term strategy prioritizes locally controlled and self\-managed AI infrastructure wherever practical to maximize security, data sovereignty, organizational knowledge retention, compliance, and operational efficiency.
This is not a traditional Full Stack Developer position.
We are seeking a hands\-on technical leader who can translate complex business operations into secure, scalable, and intelligent software systems while helping define the future technical direction of the company.
Core Mission
Design and build an AI\-powered enterprise operating system capable of:
- Automating solicitation intake and opportunity analysis
- Supporting Go/No\-Go decision\-making and capture management
- Managing proposal development and compliance workflows
- Automating contract lifecycle management
- Supporting partner relationship management and business development
- Managing project delivery and operational execution
- Supporting financial operations and reporting
- Maintaining organizational knowledge and institutional memory
- Delivering executive intelligence and business insights
- Providing employees with a secure voice\-first AI assistant capable of executing workflows and retrieving organizational knowledge
ResponsibilitiesPlatform Architecture
- Own the technical roadmap and architecture of One Algorithm's AI operating platform.
- Design modular, scalable, and maintainable systems that support long\-term business growth.
- Establish architectural standards, engineering best practices, and platform governance.
AI Systems \& Agentic Workflows
- Design and implement production\-grade AI systems utilizing LLMs, RAG architectures, vector databases, workflow orchestration, and agentic frameworks.
- Develop AI agents that automate business development, capture management, proposal development, compliance analysis, contract administration, knowledge retrieval, and operational workflows.
- Build governance frameworks for validation, auditing, monitoring, and human\-in\-the\-loop review.
Voice\-First AI Systems
- Design and implement secure voice\-enabled AI experiences for employees, leadership, and operational workflows.
- Architect conversational systems capable of interacting with enterprise applications, organizational knowledge, and business processes.
- Develop scalable frameworks for multimodal interaction across voice, chat, documents, and enterprise systems.
Enterprise Knowledge \& Memory Systems
- Design enterprise knowledge management systems utilizing Retrieval\-Augmented Generation (RAG), semantic search, vector databases, document intelligence, and organizational knowledge repositories.
- Architect short\-term and long\-term contextual memory systems that enable AI agents to securely leverage company knowledge, historical decisions, contracts, proposals, compliance artifacts, policies, and operational data.
- Develop secure retrieval frameworks that maintain access controls, auditability, and governance requirements.
Enterprise Business Systems \& Automation
- Architect and integrate systems supporting CRM, pipeline management, capture management, proposal operations, contract lifecycle management, project management, financial operations, employee lifecycle management, knowledge management, and executive decision support.
- Develop AI\-assisted Go/No\-Go decision frameworks that evaluate strategic alignment, contract eligibility, compliance risk, resource availability, teaming requirements, competitive positioning, and probability of win.
- Build systems that automate solicitation analysis, requirement extraction, compliance matrix generation, partner identification, capability matching, and proposal planning.
- Create executive dashboards that provide visibility into pipeline health, proposal status, compliance posture, operational performance, contract opportunities, and financial metrics.
- Replace manual workflows with secure, auditable, and scalable automation solutions.
Security \& Compliance
- Design systems capable of supporting federal compliance requirements including NIST 800\-171, NIST 800\-53, CMMC, RMF, FedRAMP, and secure handling of Controlled Unclassified Information (CUI).
- Implement secure development practices, audit logging, encryption strategies, access controls, and compliance\-focused architecture.
- Ensure all AI systems meet governance, security, and regulatory requirements.
Cloud Infrastructure \& Operations
- Architect and manage cloud\-native infrastructure within AWS and/or Azure environments.
- Design CI/CD pipelines, containerized deployments, monitoring solutions, observability platforms, and operational resilience strategies.
- Support secure deployment models including local, hybrid, air\-gapped, and government\-focused environments.
Engineering Leadership
- Define engineering standards, architecture governance, and security requirements.
- Mentor engineers and contribute to building a high\-performance technical culture.
- Partner directly with leadership to align technology investments with business objectives.
QualificationsSoftware Architecture \& Engineering
- 8\+ years of professional software engineering experience (10\+ years preferred).
- Demonstrated experience designing, building, and operating enterprise\-scale software systems.
- Strong foundation in software architecture, distributed systems, databases, APIs, and cloud\-native development.
- Experience owning systems from architecture through deployment, operations, incident response, and continuous improvement.
AI Systems, Agentic Architectures \& Enterprise Knowledge Platforms
- Proven experience designing and deploying production AI systems.
- Expertise with LLM integration, orchestration frameworks, Retrieval\-Augmented Generation (RAG), vector databases, and multi\-agent architectures.
- Experience designing enterprise knowledge management systems, semantic search platforms, document intelligence solutions, and agent memory architectures.
- Strong understanding of context management, memory strategies, hallucination mitigation, AI evaluation, governance, and retrieval optimization.
Enterprise Operations \& Automation
- Experience building workflow automation systems supporting complex business operations.
- Experience with CRM platforms, capture management, proposal operations, project management, contract lifecycle management, business intelligence, or enterprise operational systems.
- Experience automating processes involving compliance, contracts, finance, proposals, legal workflows, business development, or organizational operations.
Security \& Federal Compliance
- Experience developing systems for regulated, government, defense, or compliance\-driven environments.
- Knowledge of NIST 800\-171, NIST 800\-53, CMMC, RMF, FedRAMP, and secure handling of sensitive information.
- Experience implementing secure software development practices and compliance\-focused system architectures.
Cloud \& Infrastructure
- Hands\-on experience with AWS and/or Azure.
- Strong Linux administration skills.
- Experience with Docker, Kubernetes, Infrastructure as Code, CI/CD pipelines, monitoring, and observability platforms.
Leadership \& Communication
- Ability to communicate effectively with technical and non\-technical stakeholders.
- Experience mentoring engineers and establishing engineering standards.
- Comfortable operating in a fast\-moving environment where architecture decisions directly influence company growth and operational efficiency.
Compensation \& Growth Opportunity
Compensation will be structured based on experience, level of involvement, and overall contribution to the company's growth.
Opportunities may include:
- Base compensation
- Performance\-based incentives
- Contract award and project success bonuses
- Revenue participation opportunities
- Leadership advancement opportunities
- Long\-term growth participation for exceptional candidates
Compensation details will be discussed with qualified candidates during the interview process.
Location Requirement
This role is based in Malvern, Pennsylvania.
Selected candidates will participate in an in\-person technical architecture assessment and leadership discussion. Candidates should be prepared to discuss systems they have personally designed, implemented, secured, deployed, and supported in production environments.
Pay: $180,000\.00 \- $280,000\.00 per year
Work Location: In person
Salary Context
This $180K-$280K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At ONE ALGORITHM, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($230K) sits 24% above the category median. Disclosed range: $180K to $280K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
ONE ALGORITHM AI Hiring
ONE ALGORITHM has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Malvern, PA, US. Compensation range: $280K - $280K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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