Interested in this AI/ML Engineer role at AAA Washington?
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About This Role
WHAT YOU’LL DO:
Design, build, secure, and operate enterprise integrations that connect applications, data, and AI agents. Develop REST, SOAP, and event-driven integrations using MuleSoft Anypoint Platform as the preferred integration platform, along with Microsoft Azure integration services. Connect AI agents to enterprise APIs, data, and workflows using standards such as Model Context Protocol (MCP) and Agent2Agent (A2A).
WHO WE ARE:
At AAA Washington, we equip our members with trusted service and expertise to confidently travel through life. We provide our members with best-in-class roadside assistance, discounts and rewards, travel services and insurance. For over 120 years, we’ve served our members, not shareholders, so our growth supports innovation for the Club today and for the future. Seeking to reflect the communities we serve and create an inclusive culture, we encourage employees to bring their full self to work and support applicants from all backgrounds, abilities and walks of life to apply.
WHY YOU’LL LOVE WORKING AT AAA WASHINGTON:
You’ll play an important role in bringing AAA Washington’s spirit of service and peace of mind to our membership. Consistently ranked as one of the Best Places to Work by Puget Sound Business Journal, we aim for work-life balance across the organization. We support hybrid and remote work options for many positions based in our corporate office (Bellevue, Washington) along with some of the best benefits in the region!
ESSENTIAL DUTIES/RESPONSIBILITIES:
- Design, develop, test, deploy, and maintain REST, SOAP, and event-driven APIs and integrations.
- Build integration solutions using MuleSoft Anypoint Platform, Azure API Management, and Azure Functions.
- Apply API-led connectivity patterns, including System, Process, and Experience APIs.
- Implement API security using OAuth 2.0, mTLS, JWT, TLS, rate limiting, and security policies.
- Build event-driven integrations using Azure Service Bus and Azure Event Hubs.
- Connect AI agents to enterprise APIs, data services, and business workflows.
- Implement MCP for agent-to-tool integration and A2A for agent-to-agent communication.
- Develop integration and agent components using Python, Java, or C#.
- Deploy applications and infrastructure using Bicep or equivalent Infrastructure as Code (IaC).
- Build and maintain CI/CD pipelines using Azure DevOps or GitHub Actions.
- Implement automated testing, monitoring, logging, and production observability.
- Monitor and support production services using Application Insights, Azure Monitor, and Splunk.
- Troubleshoot production issues and participate in the on-call rotation.
- Maintain API specifications, technical documentation, and operational runbooks.
- Other duties as assigned.
SUPERVISORY RESPONSIBILITIES:
- None
MINIMUM QUALIFICATIONS:
- Bachelor’s degree in Computer Science, Information Systems, Business, or equivalent experience.
- Experience in API development, integration engineering, or software development.
- Experience with MuleSoft Anypoint Platform and/or Microsoft Azure integration services.
- Experience designing and developing REST, SOAP, and event-driven integrations.
- Experience with OAuth 2.0, JWT, TLS/mTLS, and API security.
- Experience with Azure Functions, Azure Service Bus, and Azure Event Hubs.
- Proficiency in Python, Java, or C#.
- Experience with Infrastructure as Code and CI/CD practices.
- Experience supporting applications and integrations in production.
PREFERRED QUALIFICATIONS:
- Experience with Salesforce Agentforce, Microsoft Copilot Studio, LangChain, Microsoft AutoGen.
- Knowledge of Model Context Protocol (MCP) and Agent2Agent (A2A).
- Experience with DataWeave, RAML, or OpenAPI Specification (OAS).
- Experience with Application Insights, Azure Monitor, and Splunk.
- Experience integrating Salesforce, Azure, SaaS, and on-premises systems.
CERTIFICATES, LICENSES, REGISTRATIONS:
- MuleSoft Certified Developer – Level 1 (MCD) preferred.
- Microsoft Certified: Azure Developer Associate (AZ-204) preferred.
- Additional MuleSoft or Microsoft Azure certifications are a plus.
KNOWLEDGE, SKILLS, ABILITIES:
- Strong knowledge of API design, integration patterns, and API-led connectivity.
- Knowledge of REST, SOAP, HTTP, messaging, and event-driven architectures.
- Knowledge of API security, including OAuth 2.0, JWT, TLS/mTLS, and access controls.
- Knowledge of MuleSoft and Azure integration technologies.
- Understanding of Agentic AI integration concepts and interoperability standards.
- Ability to write production-quality code in Python, Java, or C#.
- Ability to automate deployments using IaC and CI/CD pipelines.
- Ability to monitor, troubleshoot, and support production integrations.
- Strong analytical and problem-solving skills.
- Strong written and verbal communication skills.
- Ability to collaborate effectively with engineering, architecture, security, and business teams
SALARY:
- Hiring Range: $120,666 - $144,840
- Discretionary bonus based on company performance
BENEFITS WE OFFER:
- Health insurance (medical, vision, dental and prescription drug)
- Up to 22 days of Paid Time Off (PTO) in the first year
- Eleven paid holidays in addition to PTO
- 401(k) with Employer Match
- Life, Short-Term Disability and Long-Term Disability insurance
- Flexible Spending Accounts
- 4 Hours of Volunteer Time Off per year
- On-Demand Pay / Earned Wage Access
- Free Premier RV AAA Washington Membership
- Travel, Insurance, and Membership Discounts and Perks
- Paid, on-going training throughout your career
- THRIVE wellness program
- Tax-advantaged commuter benefit
- Free parking
- Financial Literacy Education
WORK ENVIRONMENT & PHYSICAL DEMANDS:
- Hybrid work from home and office required
- The physical environment is typical of a standard office space. It requires the employee to work inside for the duration of work. Must be able to remain in a stationary position, sitting for more than 50% of the time. This position will require occasionally moving about inside the office to access equipment, cabinets, etc. May work around office equipment which could be noisy, such as copiers and fax machines.
OTHER:
- Equipment used: Computer, phone, various virtual technologies, other office equipment
- Travel: Rarely Travel
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed are representative of the knowledge, skill, and/or ability required.
DISCLAIMER:
Position/Job characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Salary Context
This $120K-$144K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At AAA Washington, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($132K) sits 37% below the category median. Disclosed range: $120K to $144K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
AAA Washington AI Hiring
AAA Washington has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bellevue, WA, US. Compensation range: $144K - $144K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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