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About This Role
Position Information
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Hiring Manager:
Associate DirectorDepartment:
Technology SolutionsDepartment Overview
The Technology Solutions (“TS”) Department is responsible for designing and delivering state of the art technology solutions that are designed to create efficiency, mitigate risk and grow revenue for the Firm. Technology Solutions is also responsible for defining, managing and executing a robust Cyber Security program following the NIST Cyber Security Framework. Technology Solutions focuses on technical excellence through innovative application designs, robust data integration and analytics, high availability infrastructure and gold level service for our key stakeholders with information security embedded throughout. Critical functions within Technology Solutions include Project Management, Vendor Management, Business Analysis, Enterprise Data Governance and Stewardship, Application Development and 3rd Party Integration, Strategic and Secure Infrastructure and Operations. The Technology Solutions Department collaborates closely with Firm leadership and business unit heads to develop plans in line with business objectives.
Position Responsibilities
We are seeking an Assistant Vice President to fundamentally reimagine how work gets done across the Firm. The role will partner directly with business stakeholders to assess end\-to\-end functions (aligned to business domains such as Sponsor Finance or other investment teams, Investor Partners Group, Back Office or other core areas of the Firm), challenge traditional operating assumptions and redesign workflows, decision\-making processes and operating models through the practical application of artificial intelligence. This is not a software engineering role; rather, this position is focused on AI\-native business process transformation, helping business teams rethink how work should be performed when modern AI capabilities such as agentic systems, enterprise context layers, knowledge\-grounding frameworks and human\-in\-the\-loop workflows are available as core design tools. The ideal candidate combines expertise in business process transformation, operating model design and organizational change with deep understanding of modern AI capabilities and their practical application to deliver tangible business outcomes.
Responsibilities include:
- Partnering with business leaders to identify, prioritize and roadmap AI transformation opportunities that enhance operational efficiency, decision\-making and business outcomes
- Analyzing business processes, workflows decision pathways and knowledge flows to design AI\-native operating models that automate, augment and reimagine how work gets done across teams and business functions
- Conducting current\-state and future\-state assessments to identify opportunities for transformational change, challenging existing assumptions and redesigning business processes using AI\-first design principles
- Partnering with AI engineering, architecture, data and delivery teams to translate future\-state business process designs into executable roadmaps and enterprise AI solutions
- Developing scalable AI transformation frameworks, reusable operating patterns and best practices that enable repeatable business transformation across the Firm
- Developing transformation business cases, value realization frameworks and success metrics to ensure AI initiatives deliver measurable business outcomes.
- Ensuring AI solutions are secure, compliant and aligned with enterprise architecture, governance and data readiness standards
- Partnering with organizational change management to enact user adoption and value realization efforts to maximize the impact of AI\-enabled business transformation
- Evaluating emerging AI technologies and industry trends, translating new capabilities into practical business applications and measurable outcomes
- Fostering a culture of AI innovation by leading communities of practice, mentoring team members and advancing enterprise AI capability and knowledge
Candidate Requirements
*Qualifications \& Experience:*
- Bachelor’s degree in computer science, business or related field is required
- 5\+ years of experience in software delivery, product management, technology consulting or business process transformation, with demonstrated experience applying AI concepts and patterns to real\-world business challenges, with sufficient technical depth to assess feasibility, tradeoffs and determine appropriate solution approaches
- Deep understanding of modern AI capabilities, architectures and design patterns, including AI agents, enterprise context layers, Model Context Protocol (MCP), context engineering, harness engineering, knowledge grounding strategies, model evaluation frameworks, tool integration patterns, reasoning models and human\-in\-the\-loop systems, with the ability to apply these concepts to redesign business processes and operating models
- Familiarity with AI orchestration frameworks, multi\-agent systems, Retrieval Augmented Generation (RAG) and enterprise search / vector databases is highly desirable
- Demonstrated experience leading business process re\-engineering, operating model redesign, management consulting or strategic transformation initiatives in partnership with business stakeholders
- Ability to apply AI\-first design principles to business challenges, reimagining workflows and operating models based on what becomes possible through emerging AI capabilities rather than simply optimizing existing processes
- Experience in strategic planning and roadmap development for AI initiatives
- Experience facilitating workshops, discovery sessions, current\-state assessments and future\-state design engagements with business and executive stakeholders
- Deep understanding of how data is processed, including data ingestion, storage and retrieval using AI services, particularly Azure OpenAI, Azure AI Foundry, Fabric IQ and RAG architecture
- Ability to estimate and establish cost baselines and financial monitoring (ROI / TCO) that quantify the expected business value of AI\-enabled transformation initiatives
- Experience developing business cases, forecasting value realization and measuring operational impact from technology\-enabled transformation initiatives
- Proficiency with software development principles; Python familiarity a plus
- Demonstrated ability to lead change, drive adoption of new ways of working and influence stakeholders through organizational and technical transformation
- Strong analytical, problem\-solving, communication and collaboration skills, with the ability to partner effectively across business and technical teams
- Client\-focused, self\-motivated professional who thrives in fast\-paced, complex environments and effectively prioritizes multiple competing demands
- Enthusiastic about working in office and creating a Gold Standard hybrid work culture
Critical Competencies for Success
Our Gold Standards define key behaviors and competencies across 4 dimensions: Leadership, Achieving Results, Personal Effectiveness and Thinking Critically. These behaviors and competencies drive our ability to win together.
- Leadership: Role models in this area consistently focus on the right goals and priorities and continually develop themselves and others. Always team players, they influence and engage with others to contribute to a supportive and inclusive culture where all feel welcome.
- Achieving Results: Role models in this area are high achievers who develop careful plans and deliver consistently and effectively. They hold themselves and others accountable for delivering high quality results, and they remove barriers to ensure others can contribute and grow.
- Personal Effectiveness: Role models in this area build strong relationships, treat others with respect and communicate effectively. They are driven to exceed expectations and are adaptable to changing circumstances.
- Thinking Critically: Role models in this area understand our business, rely on analytical reasoning and seek diverse perspectives to solve problems. They are forward thinking, anticipating issues and addressing them in advance.
The department\-specific competencies define the knowledge, skills and abilities that are needed to successfully perform the functional or technical work of this role.
- Technical Support: Triages, troubleshoots and resolves technical support issues. Escalates issues as needed.
- Software Development Principals: Utilizes software development, secure programming principles and a knowledge of programming languages to develop, configure and / or integrate new software and applications.
- Business Needs Assessment: Identifies business needs across departments within the Firm to understand the challenges, goals and problems that the business needs to solve and identifies appropriate technical solutions.
- Data Management and Information Security: Manipulates, restructures and / or queries data for various purposes, including reconciling issues in the database, designing database structures and / or generating reports. Adheres to governance principles and maintains data integrity and security.
- Risk Management: Identifies, forecasts and articulates ways to pursue and manage informed risks in ambiguous, complex or uncertain situations based on sound value propositions and an analysis of potential rewards and costs.
- Testing: Evaluates the functionality of an application, system or solution to ensure that requirements have been met and defects have been identified. Applies an understanding of end user requirements and usage in the end\-to\-end system to produce a quality product.
- Industry Knowledge: Demonstrates an understanding of the Firm's position in the industry, including its complex structure and competitive advantage in the marketplace. Monitors industry trends and changes and recognizes their relevancy and implications.
- Technical Communication and Documentation: Documents and communicates technical processes and procedures in area of specialty to stakeholders. Adapts the level of detail and specificity based on the needs of the intended audience.
- Vendor Management: Manages and coordinates with external vendors. Researches and identifies new vendors as needed and monitors performance.
- Relationship Management: Builds and maintains effective partnerships with internal clients and end users by advising on their needs and options, advocating for their business within the Technology Solutions department and managing expectations appropriately.
- Project / Program Management: Manages Technology Solutions project elements considering conflicting priorities, interdependencies, business objectives, communications and available resources.
- Innovative Mindset: Leverages an agile and creative mindset to drive innovative value creation, continuous process improvement and proactive learning through new technology, processes and people.
Compensation and Benefits
For Illinois and New York Only: It is expected that the base salary range for this position will be $95,000 to $150,000\. Actual salaries may vary based on factors such as skills, experiences and qualifications for the role. The total compensation package for this position may also include other elements and discretionary awards in addition to a full range of medical, financial and / or other benefits (including 401(k) eligibility and various paid time off benefits such as vacation, sick time and parental leave) dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, the employee will be in an ‘at\-will position’ and the Firm reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time including for reasons related to individual performance, Firm or individual department / team performance and market factors.
Golub Capital is an Equal Opportunity Employer.
*Due to the highly regulated nature of Golub Capital’s business and because of the sensitivity of the information that all personnel have access to, Golub Capital performs extensive and thorough pre\-hire screens to ensure that its personnel act with expected levels of integrity, professionalism and personal responsibility.*
Please review Golub Capital’s US Job Applicant privacy notice and, for California residents, the California Applicant privacy notice for information on how your personal data is collected, processed and stored.
Salary Context
This $95K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Golub Capital, 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 $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($122K) sits 43% below the category median. Disclosed range: $95K to $150K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Golub Capital AI Hiring
Golub Capital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $150K - $150K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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