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About This Role
Senior Software Engineer \- AI Platform
Location: Remote
Salary Range: $140,541\- $223,212, per year, depending on experience and qualifications.
Employment Type: Full\-time
What you can expect as a Senior Software Engineer \- AI Platform at Fortress:
*Fortress Information Security is seeking a Senior Software Engineer, AI Platform to help build the foundational AI platform powering intelligent workflows across Fortress products and services. This role focuses on designing and operating the orchestration, retrieval, execution, and platform infrastructure required to support scalable, secure, and reliable AI systems in highly regulated environments.*
*The ideal candidate brings strong distributed systems and backend engineering experience combined with hands\-on experience building production\-grade AI infrastructure, orchestration systems, or retrieval platforms. This individual will work across platform, infrastructure, and application layers to help establish the technical foundation for Fortress’s AI strategy.*
*This role is well\-suited for engineers who enjoy solving complex systems problems involving long\-running stateful workflows, multi\-store retrieval architectures, event\-driven systems, observability, and secure multi\-tenant infrastructure.* *Please note:* *This role is built for a true, current senior engineer. Someone who hits the ground running, drives decisions independently, and raises the bar for the team from day one.*
Responsibilities Include:
- Design, build, and operate orchestration frameworks supporting long\-running, stateful AI workflows and agent execution systems
- Develop distributed retrieval architectures spanning vector, graph, search, and relational data platforms
- Build shared platform services, reusable runtime components, and tool registries supporting AI capabilities across Fortress products
- Implement resilient event\-driven and change\-data\-capture (CDC) pipelines with strong consistency, retry, and fault\-tolerance guarantees
- Develop typed APIs and integration contracts supporting AI workflows and platform interoperability
- Improve scalability, performance, reliability, and operational efficiency across orchestration and retrieval infrastructure
- Support secure multi\-tenant deployments across cloud, hybrid\-cloud, and air\-gapped environments
- Build and maintain observability, telemetry, tracing, centralized logging, governance, and policy enforcement tooling
- Contribute to engineering standards and architectural patterns for orchestration, retrieval, and platform infrastructure
- Collaborate cross\-functionally with platform engineering, product engineering, security, and AI teams to support platform adoption and operational excellence
- Participate in troubleshooting, incident response, root cause analysis, and operational support activities related to platform infrastructure
- Continuously evaluate emerging technologies, tooling, and engineering practices relevant to AI infrastructure and distributed systems
Minimum Qualifications:
- 6\+ years of experience in backend, platform, or infrastructure engineering building production distributed systems
- Strong Python development experience including asynchronous systems, API design, and typed architectures
- Experience designing and operating scalable distributed or service\-oriented systems in production environments
- Hands\-on experience building or operating AI infrastructure, orchestration systems, retrieval platforms, or modern RAG architectures in production
- Strong understanding of distributed systems concepts including queues, event streams, retries, consistency models, concurrency, and fault tolerance
- Active daily use and fluency with AI\-assisted engineering workflows and agentic development tooling such as Claude Code, Cursor, or similar platforms, including effective use of LLMs throughout software design, implementation, debugging, and operational workflows.
- Strong written and verbal communication skills with the ability to collaborate effectively across technical teams
- Ability to operate independently in fast\-moving, highly technical environments with evolving priorities
Preferred Qualifications:
- Experience with orchestration and workflow frameworks such as LangGraph, Temporal, Celery, or similar technologies
- Experience with vector databases, graph databases, search platforms, and modern retrieval architectures involving embeddings, hybrid search, reranking, or knowledge graph traversal
- Experience with event\-driven architectures, CDC pipelines, or streaming platforms such as Kafka, Pulsar, or pg\-boss
- Experience with observability and telemetry tooling such as OpenTelemetry, Prometheus, Grafana, or centralized logging platforms
- Experience deploying and operating systems within regulated, high\-security, hybrid\-cloud, or air\-gapped environments
- Familiarity with secure software supply chain practices, signed artifact distribution, and multi\-tenant platform isolation strategies
- Contributions to internal developer platforms, reusable infrastructure tooling, or shared engineering frameworks
- Experience mentoring engineers and contributing to engineering standards and architectural best practices
Education:
- Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related technical field preferred. Equivalent combinations of professional experience, technical training, certifications, or demonstrated expertise will also be considered.
Who thrives in this Role:
*This team moves fast, operates with high autonomy, and builds things that don't exist yet. Engineers who do well here are genuinely deep in AI infrastructure, treat AI\-assisted development as a core skill they're actively improving, and prefer building over debating. They own their outcomes without being pushed and find constrained, regulated environments interesting rather than frustrating.*
*If you've been building in this space on your own time and want to work somewhere that takes it seriously, this role is for you.*
This role is not for you if...
- You're drawn to AI but haven't actually built anything in the space.
- You treat AI development tools as a novelty rather than a daily practice.
- You do your best work with clear roadmaps, stable requirements, and plenty of process.
- You want to architect and delegate rather than write the code yourself.
- You need direction to get moving.
Employee Benefits:
- Remote and Hybrid working environment
- Competitive pay structure
- Medical, dental, vision plans with employees covered up to 90% with highly progressive options for dependents and families
- Company paid life, short\- and long\-term disability insurance
- Employee Assistance Program
- 401(k) match
- Flexible Paid Time Off
- Parental Leave
- Access to thousands of Learning \& Development courses that range from mental health and wellbeing, stress, and time management to an array of technical and business\-related courses
Employment Perks:
- We provide each employee with professional growth opportunities through succession planning, up\-skilling, and certifications
- Tuition and certification reimbursement
- Employee Referral Programs
- Company Sponsored Events
Fortress is proud to be an Equal Opportunity Employer. All employees and applicants will receive consideration for employment without regard to age, color, disability, gender, national origin, race, religion, sexual orientation, gender identity, protected veteran status, or any other classification protected by federal, state, or local law. Fortress Information Security takes part in the E\-Verify process for all new hires.
For positions located in the US, the following conditions apply. If you are made a conditional offer of employment, you will have to undergo a drug test. ADA Disclaimer: In developing this job description care was taken to include all competencies needed to successfully perform in this position. However, for Americans with Disabilities Act (ADA) purposes, the essential functions of the job may or may not have been described for purposes of ADA reasonable accommodation. All reasonable accommodation requests will be reviewed and evaluated on a case\-by\-case basis.
Salary Context
This $140K-$223K range is below the median for AI Software Engineer roles in our dataset (median: $190K across 193 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 3,824 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Fortress Information Security, 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 $234,620 based on 682 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($181K) sits 22% below the category median. Disclosed range: $140K to $223K.
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.
Fortress Information Security AI Hiring
Fortress Information Security has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US. Compensation range: $223K - $223K.
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 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 3,824 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).
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 3,824 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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