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About This Role
Fusion Risk Management is a fast-growing, innovative company committed to fostering a supportive, inclusive environment and recognized by the Chicago Tribune and Built in Chicago for its culture!
Fusion is a leading provider of cloud-based software solutions for operational resilience, encompassing risk management, third-party risk management, information technology and security risk, business continuity and disaster recovery, and crisis and incident management. Recognized by Gartner, Forrester, Deloitte, and more, we seek to build a more resilient world by empowering organizations to make data-driven decisions and helping them achieve greater overall resilience.
Our flagship product, The Fusion Framework® System™, delivered on the Salesforce Lightning Platform, provides companies a North Star for operational resilience. Fusion serves mid-size organizations to Fortune 50 companies across various verticals including financial services, manufacturing, energy and utilities, retail, pharmaceuticals, and education.
We are looking to add talented individuals to our team who are passionate about our vision to build a resilient world together and inspired by the challenge of solving key business problems. We seek can-do people who fit the culture, align with our core values, and prioritize continued personal and professional development. If this sounds like you, read on!
Core Values
Our values are at the center of our company. They are the core ethics and principles that help define our personality as an organization and help give us focus and purpose. They are overarching, building blocks of our culture and can always be used as a consistent reference point our company-wide ethics.
- Trust: Earn teammates’ trust and assume positive intent; act with integrity; respect diversity of thought, skills, and background
- Passion: Make a difference; don’t wait until you’re asked or instructed; maintain a bias to action and impact
- Collaboration: Think systematically and see your role within the bigger picture; be accountable for your part of team success; put your teammates in a position to thrive
- Customer Centricity: Demonstrate a solid commitment to customer success by providing positive and consistent customer experiences (exhibiting professionalism, patience, respect, and knowledge); Proactively engage our customer community, seek customer feedback – be open to listening – and actively incorporate the voice of the customer into daily activities
- Growth: Strive for excellence; embrace change; prioritize continuous improvement
The Role
Fusion Risk Management is seeking a hands-on technical executive to serve as Vice President, Technical Architecture & AI Transformation. Reporting to the Chief Customer Officer (CCO), this role leads Fusion's Technical Architecture practice while personally building and directing the development of AI tooling used by Professional Services.
This is a dual-mandate leadership role. On the AI side, the Vice President writes code and ships production AI tooling directly, working alongside a full-stack engineer and architecture team members dedicated to that work. On the architecture side, this leader manages and mentors Fusion's technical architects, establishes practice standards, supports complex customer engagements, and serves as the senior technical authority for enterprise and regulated customers.
As Fusion evolves into a multi-platform company - built on Salesforce today and expanding to ServiceNow - this leader will bring deep architectural expertise in at least one of these platforms, with Salesforce preferred, and develop sufficient depth in the other to lead architects effectively and engage credibly with prospective customers' enterprise architecture and security teams.
Responsibilities
Technical Architecture Leadership
- Lead and mentor the Technical Architecture team, setting technical standards, operating practices, and direction for the function.
- Serve as Fusion's senior technical escalation point and architectural authority for enterprise and regulated customers.
- Own hiring, promotion, compensation, performance management, and career development for the Technical Architecture practice.
- Build the team's capability on a second enterprise platform as Fusion expands to ServiceNow alongside its Salesforce foundation.
- Own the evaluation and modernization of core delivery infrastructure, architecture standards, and technical tooling.
- Participate in deal-scoping governance before delivery commitments are made, helping ensure proposed solutions are technically sound, secure, and deliverable.
AI Tooling & Delivery Transformation
- Personally build production AI tooling used internally by Professional Services while directing development work performed by a dedicated full-stack engineer and participating architecture team members.
- Define the technical architecture, development standards, and roadmap for AI-enabled delivery tooling that improves the quality, consistency, and efficiency of Professional Services work.
- Establish the guardrails, security controls, and operating model required to use AI safely against live customer environments and sensitive enterprise data.
- Drive adoption across Professional Services through structured rollout, enablement, training, feedback loops, and continuous improvement.
- Evaluate emerging AI and LLM capabilities for practical application within Fusion's delivery model and translate promising capabilities into production-ready tools.
Presales Support
- Support the Sales team in prospect and customer engagements by answering technical questions about Fusion, solution architecture, integrations, security, and the underlying platform ecosystem.
- Engage credibly with enterprise architecture, security, and technical stakeholders during complex sales cycles, particularly for large and regulated organizations.
- Partner with Sales and Professional Services leadership to identify technical risks, dependencies, and delivery considerations before commitments are finalized.
Knowledge, Skills, and Abilities
Technical Architecture & Platform Expertise
- Deep architectural expertise in Salesforce or ServiceNow, with Salesforce strongly preferred; ability and willingness to build credible architectural depth in the second platform.
- For Salesforce-focused candidates, strong command of Apex, Lightning Web Components (LWC), Flow, integrations, security, data architecture, and enterprise platform design patterns.
- Experience operating at or above a Senior Solutions Architect level, with the technical standing to mentor architects and set direction for a team performing complex solution-design work.
- Strong understanding of enterprise integrations, APIs, identity and access management, data security, deployment patterns, and architecture considerations in regulated customer environments.
- Relevant platform architecture certifications desired.
AI Engineering & Delivery Transformation
- Demonstrated experience building and shipping production AI- or LLM-based tooling, with the ability to contribute directly to code rather than solely manage AI initiatives.
- Practical understanding of LLM application architecture, prompt and context design, model/tool integration, evaluation, observability, and production deployment considerations.
- Experience defining guardrails and controls for AI systems operating with customer data or against live customer environments.
- Ability to identify high-value delivery workflows for automation and translate them into secure, maintainable tools that teams will adopt.
- Agile development methodology expertise desired.
Leadership & Customer Engagement
- Experience managing technical teams, including hiring, compensation, promotion, performance management, coaching, and career development.
- Demonstrated experience serving as a customer-facing technical authority for large, complex, and regulated enterprises.
- Strong communication and executive presence, with the ability to translate complex technical concepts for customer, commercial, and executive audiences.
- Strong technical project management and cross-functional collaboration skills across Professional Services, Sales, Product, Engineering, Security, and Customer teams.
- Business acumen to balance customer requirements, product strategy, delivery realities, and long-term platform direction while clearly communicating the value of the Fusion Framework System.
Qualifications (Education and Experience)
- Significant progressive experience in enterprise solution architecture, technical consulting, professional services, or a comparable customer-facing technical function.
- Demonstrated leadership of architects or senior technical resources responsible for designing and delivering complex enterprise solutions.
- Hands-on software development experience sufficient to build, review, and ship production AI tooling in partnership with engineering resources.
- Experience with Salesforce architecture strongly preferred; ServiceNow architecture experience is also highly relevant, particularly for candidates able to develop depth across both ecosystems.
- Experience supporting enterprise presales, technical discovery, solution scoping, and architecture/security discussions with sophisticated customer stakeholders.
- Relevant Salesforce, ServiceNow, cloud, security, or enterprise architecture certifications preferred.
Compensation & Benefits
The annual base salary for this position is $200,000. This role is also eligible for bonus potential. Fusion offers a comprehensive benefits package including medical, dental, vision, and a 401(k) plan.
Disclaimers:
Fusion is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, age, pregnancy, military service or discharge status, genetic information, sex, sexual orientation, gender identity, or national origin. Nothing in this job posting should be construed as an offer or guarantee of employment.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Fusion Risk Management, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Fusion Risk Management AI Hiring
Fusion Risk Management has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in 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/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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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).
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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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