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About This Role
About JSSI
Founded in 1989 and headquartered in Chicago, Jet Support Services, Inc. (JSSI) is the largest independent provider of hourly cost maintenance programs for business aviation. JSSI delivers comprehensive coverage for engines, airframes, and auxiliary power units (APUs) across more than 300 different aircraft makes and models, helping owners and operators stabilize maintenance budgets, maximize aircraft availability, and protect asset value throughout the lifecycle of ownership. JSSI has built a portfolio of complementary business lines designed to simplify the economic and technical complexity of business aviation; these include Maintenance teams, Traxxall maintenance tracking software, Parts & Engines, Conklin aircraft cost and performance data, and Aviation Capital asset-based financing solutions. Together, these offerings support owners, operators, and maintenance providers with integrated tools spanning ownership and maintenance planning, execution, and financial management. With more than 6,500 aircraft supported through programs and software platforms, JSSI leverages its unique independence, unmatched scale, and data-driven insight to deliver customized solutions and support models aligned to the interests of each client — regardless of aircraft platform. JSSI is backed by leading institutional investors GTCR, Genstar Capital, and Blackstone. Learn more at jetsupport.com.
Position Summary:
As Director, AI First Enterprise Architecture, you define and own the enterprise architecture vision and standards that shape how JSSI builds software in an AI First, agentic engineering model. Reporting to the SVP of Engineering & AI Transformation, you set the reference architectures, API and integration standards, and architectural guardrails that every engineering vertical follows, converting JSSI’s investment in Claude Code Enterprise and the Microsoft/Azure platform into consistent, secure, and scalable systems.
This is a hands-on, high-influence leadership role. You chair JSSI’s Architecture Review Board (ARB), where you advocate and uphold shared architecture standards across the AI Solution Architects embedded in each engineering vertical. You directly lead two AI Solution Architects who serve as your enterprise architecture team, and you partner closely with the Directors of Engineering so that delivery teams build on common patterns rather than divergent one-offs.
Success is measured by outcomes: the consistency and reuse of architecture across the organization, the security and reliability of what teams ship, the speed at which proven patterns spread, and the trust the business places in JSSI’s engineering decisions. You will balance architectural rigor with delivery pace, holding clear human accountability for AI-generated systems while driving the organization toward its AI First goals.
Position Responsibilities:
AI First Enterprise Architecture & Strategy
- Define and own JSSI’s AI First enterprise architecture vision, aligning agentic engineering, product platforms, data strategy, and modernization with business priorities across maintenance, parts, leasing, programs, asset management, and customer operations.
- Architect enterprise-scale AI ecosystems spanning multi-agent systems, retrieval-augmented generation (RAG) and knowledge platforms, MCP-enabled tooling, model lifecycle management, and AI-assisted SDLC capabilities.
- Establish the Microsoft/Azure-first architecture posture (C#/.NET, ASP.NET Core, React/TypeScript, Azure SQL, Service Bus, Entra ID, Key Vault, App Services, AKS/ACR, Application Insights, and secure CI/CD) as the default for new and modernized systems.
- Operationalize Claude Code Enterprise as a strategic accelerator across specification, code generation, refactoring, testing, documentation, and review, supporting JSSI’s goal of 100% AI-generated code with human accountability for intent, architecture, and quality.
Architecture Review Board, Standards & Governance
- Chair the Architecture Review Board (ARB), setting the agenda, cadence, and decision rights that drive consistent architecture across engineering verticals.
- Advocate and uphold shared standards across the AI Solution Architects embedded in each vertical, ensuring teams build on common patterns, contracts, and guardrails rather than divergent implementations.
- Define and maintain API design, event-driven, data contract, versioning, and interoperability standards, along with the agent behavior boundaries and evaluation frameworks that keep AI deployment responsible and predictable.
- Establish governance for AI First development: code ownership, review expectations, IP and secrets protection, auditability, approved model usage, and human-in-the-loop accountability, in partnership with Security, Legal, and Infrastructure.
- Ensure responsible AI across everything that ships: fairness, explainability, model monitoring, security posture, and regulatory alignment.
Reference Architectures & Platform Alignment
- Publish and mature reference architectures, architecture decision records (ADRs), reusable patterns, and engineering playbooks that improve delivery speed without sacrificing quality.
- Standardize how teams build and consume MCP servers that expose JSSI enterprise systems (Salesforce, Dynamics 365 F&O, Microsoft Fabric, and proprietary products) as secure, model-ready tools.
- Ensure AI systems and automation integrate reliably with JSSI’s enterprise platforms, coordinating with the data and platform teams that own them.
- Define observability, lifecycle-management, performance, and resiliency standards that keep production AI systems dependable as they scale.
- Drive measurable architecture maturity through ADR discipline, pattern reuse, API consistency, reduced rework, and adoption metrics.
Project Intake, Discovery & Emerging Capability Adoption
- Serve as the architectural intake gate for new initiatives, assessing incoming project and product requests for architectural fit, AI suitability, feasibility, build-versus-buy options, risk, and alignment to enterprise standards before they enter delivery.
- Provide clear intake recommendations and sizing inputs to the SVP, Product, and engineering leaders so the organization commits to the right work with full visibility into architectural impact.
- Maintain a forward view of the AI and agentic landscape (models, tools, frameworks, and vendor capabilities) so JSSI knows what is coming and can plan deliberately rather than react.
- Run structured discovery, proofs of concept, and capability spikes that validate promising technologies against JSSI’s Microsoft/Azure stack, security posture, and business needs.
- Define the pathway for accepting proven capabilities into production: how new tools and patterns are vetted, standardized, folded into reference architectures, and adopted across teams.
Leadership, Mentorship & Stakeholder Partnership
- Directly lead and develop two AI Solution Architects, owning their direction, coaching, and growth as the enterprise architecture team.
- Guide and influence the AI Solution Architects and engineering leaders across verticals through the ARB and technical leadership rather than direct management.
- Stay close enough to the work to prototype, review designs, challenge assumptions, and demonstrate credible hands-on engineering judgment, setting the standard the organization is expected to meet.
- Build trusted relationships across Business Units, Engineering, Product, Architecture, Security, Infrastructure, Data, and Operations, translating business needs into sound architectural direction and a prioritized pattern roadmap.
- Translate architecture strategy, risks, adoption, and business impact into clear executive-level narratives and operating reviews.
Required Qualifications:
Core Experience
- 12+ years in software engineering and architecture, including 5+ years in a solution architect, principal engineer, or enterprise/product architecture leadership role with ownership of system design for production SaaS platforms.
- Demonstrated experience defining architecture strategy for cloud, distributed systems, APIs, integrations, and data platforms at enterprise scale.
- Production-level fluency in the Microsoft stack (C#/.NET, ASP.NET Core, REST APIs, React/TypeScript, SQL Server / Azure SQL) and distributed-systems patterns such as queues, caching, and scalable APIs.
- Hands-on experience designing and implementing AI First, spec-driven (SDD) workflows and agentic systems across the software delivery lifecycle.
- Track record establishing architecture standards, review processes, and governance that improved consistency, security, and delivery speed across multiple teams.
- Proven ability to influence executives and cross-functional leaders, and to mentor senior architects and engineers through influence rather than direct people management.
- Excellent written and verbal communication skills; able to translate complex technical concepts for technical and non-technical audiences alike.
AI & Claude Ecosystem Proficiency (Claude Strongly Preferred)
- Hands-on experience with AI coding agents and agentic workflows (Claude Code strongly preferred; GitHub Copilot, Codex, or equivalent), including CLI integration and multi-agent pipelines.
- Hands-on experience with multi-agent design patterns (planning, orchestration, observability) and MCP server implementation against enterprise data sources.
- Production expertise with LLM APIs (Claude API preferred): tool use, structured outputs, document processing, streaming, and rate-limit management.
- Strong prompt engineering skills, including structured outputs and retrieval-augmented prompting.
- Working knowledge of AI evaluation frameworks (quality, cost, latency) and responsible-AI design aligned with Anthropic’s principles.
Highly Desired Qualifications
- Experience in aviation, transportation, asset management, maintenance, aftermarket services, or other complex operational B2B domains.
- Experience with Azure AI Foundry, Azure OpenAI, Microsoft Fabric/OneLake, Semantic Kernel, Power Platform, or comparable AI engineering ecosystems.
- Experience modernizing legacy platforms through incremental architecture, API-led integration, event-driven patterns, cloud migration, and test automation.
- Familiarity with DORA metrics, SPACE concepts, and developer productivity telemetry.
- Integrations with Dynamics 365 F&O, Salesforce or equivalent CRM, and Microsoft Fabric.
- Experience in regulated, safety-sensitive, or audit-heavy environments where security, governance, and operational reliability matter.
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent professional experience; advanced degree a plus.
At JSSI, we are committed to rewarding our team members with competitive and comprehensive compensation packages that reflect their contributions and talent. For this role, the annual base pay generally ranges from $170,000 to $220,000, with the final offer determined by variable factors including but not limited to market location, job-specific knowledge, skills, education, and experience. In addition, your total rewards package may include an annual discretionary bonus plan, incentive or sales bonus plan, or other form of additional compensation, based on your role. Additionally, for full-time employees based in the USA, Canada, or the Philippines, we are proud to offer a robust suite of benefits. This ranges from insurance offerings such as medical, dental, vision, retirement savings programs, among others, starting day one of employment. For a full overview of our offerings, visit the JSSI Benefits Page on our company website.
JSSI is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or other characteristic protected by law.JSSI does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or Recruiting Team.
Pay: $170,000.00 - $220,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Parental leave
- Professional development assistance
- Referral program
- Retirement plan
- Vision insurance
Work Location: Hybrid remote in Chicago, IL 60607
Salary Context
This $170K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Jet Support Services, Inc., 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 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($195K) sits 8% below the category median. Disclosed range: $170K to $220K.
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.
Jet Support Services, Inc. AI Hiring
Jet Support Services, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $220K - $220K.
Location Context
AI roles in Chicago pay a median of $194,700 across 390 tracked positions. That's 9% 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 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).
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 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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