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About This Role
Location: Hybrid- Employees are required to work 3 days out of the nearest office location.
Aravo Solutions, Inc., provides leading third-party risk management (TPRM), ESG, and vendor lifecycle management solutions powered by intelligent automation software and designed to meet the needs of TPRM teams at Global 2000 enterprises.
At Aravo, we believe that in an increasingly interconnected world, managing risk is a critical pillar of global resilience. Traditional, manual approaches to TPRM can no longer keep pace with today’s scale and speed of disruption. That’s why we have pioneered an Intelligence-First™ approach.
Trusted by millions of third-party users in 195+ countries, Aravo provides guidance for the most complex third-party networks in the world, helping them mitigate risk, build business resilience, and protect their reputations. We are fundamentally redesigning how organizations see, understand, and act on risk by embedding AI directly into the core of their workflows.
Joining Aravo means working at the forefront of AI in TPRM and collaborating with a team that views AI as a partner, not a replacement. You will have the opportunity to work alongside industry experts, leverage the latest technologies, and contribute to shaping the future of third-party risk management!
Position Overview:
We are seeking a highly skilled Senior Software Engineer to help design and build our AI agent platform — including autonomous agents that process data, retrieval-augmented generation (RAG) systems, and infrastructure for deploying models in production. This role is critical to expanding our platform's AI capabilities, enabling agents to map outputs onto structured data models, and scaling GPU-based model deployment.
You will work closely with Product Management, DevOps, and other engineering teams to align technical decisions with business goals while delivering progressively more capable and high-performing AI/agent functionality.
This role requires strong experience across both backend systems engineering and applied AI/ML, with a significant focus on production-grade agent tooling and retrieval-based systems.
Requirements Key Responsibilities:
- Collaborate closely with Product Management to translate complex business and technical concepts into actionable deliverables for a multi-tenant, cloud-native AI platform
- Design and extend tooling that maps AI outputs onto structured, customer-specific data models
- Build and evolve RAG pipelines for document parsing and question answering
- Develop and maintain multiple agent types (e.g., classification, document parsing, custom workflows), including authentication, error handling, and observability
- Design for scale, supporting high availability, multi-tenancy, and rapid growth
- Deploy and operate ML models on GPU infrastructure in production
- Ensure integrations meet security, compliance, and performance requirements
- Provide technical leadership through design reviews, code reviews, and mentorship of other engineers
Qualifications:
Required
- Bachelor's in Computer Science, Engineering, or equivalent experience in a related field
- 7+ years of software engineering experience, with a strong focus on Java-based systems
- Proficiency in Python, including AI/ML libraries and PDF extraction
- Deep understanding of NLP, neural networks, and LLMs, with the ability to implement concepts from technical research papers
- Extensive experience building and consuming RESTful APIs
- Strong data architecture skills, including database design, data modeling, and schema management with relational and non-relational databases
- Use of AI technologies in production systems
- Excellent communication, organizational, and interpersonal skills
- Agile/Scrum or other iterative methodology experience
Preferred Qualifications/Skills/Soft Skills:
- Adept at designing solutions in public, private, and hybrid infrastructure models
- Knowledge of AI infrastructure, LLM APIs, and experience with Model Context Protocol (MCP) or similar AI agent frameworks
- Experience deploying ML models to GPUs in production environments
- Experience with enterprise integrations or third-party/vendor systems
- Familiarity with API gateways and security standards (OAuth, JWT)
- Comfortable in Unix/Linux scripting environments
Why Join Us:
At Aravo Solutions, Inc., we offer a dynamic and inclusive work environment where employees are encouraged to innovate, grow, and make a meaningful impact. We provide competitive compensation and benefits packages, opportunities for professional development, and the chance to work alongside industry experts on cutting-edge projects.
Benefits* 100% Employer Paid Medical Insurance options for the Employee and Family
- Paid Maternity and Paternity Leave
- Life and AD&D Insurance
- Long-Term Disability Insurance
- 401K with Company Matching
- Equity Participation
- 4 Weeks of Vacation
- Fully Stocked Kitchens
- Company-Sponsored Charitable Day of Giving Events
As a SaaS company, we're all about smart technology and forward-thinking innovation. But when it comes to building our team, we're firm believers that people should evaluate people.
That’s why we’ve intentionally turned off the AI-driven screening and scoring features in our Applicant Tracking System (ATS). When you apply to Aravo, your application isn't filtered out by an invisible algorithm—it's reviewed by a real recruiter working hand-in-hand with our hiring managers. We've also established and enforced a companywide policy governing the appropriate use of AI, including how it can enhance the hiring process for managers without relying on an invisible, unknown entity to make decisions.
If you're taking the time to learn about us and put your best foot forward, we're committed to matching that effort with ours. Technology might power our platforms, but when it comes to recognizing potential, real people will always remain at the heart of our hiring decisions.
Aravo Solutions Inc. is registered as an employer in many, but not all, states. If an applicant is not in or able to work from a state where Aravo Solutions Inc. is registered, they may not be eligible for employment. The eligible states include: FL, GA, MA, MO, NC, NH, NV, OR, PA, SC, TN, and TX.
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 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Aravo Solutions, 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 $220,000 based on 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Aravo Solutions, Inc. AI Hiring
Aravo Solutions, Inc. has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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