Interested in this AI/ML Engineer role at Gen II Fund Services?
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About This Role
Head of AI, Data and Process Transformation Who is Gen II? Gen II is a leading fund administration provider focused entirely on serving private capital asset managers and investors with a best-in-class combination of people, process, and technology. Gen II has more than $2 trillion in private fund capital assets under administration.
With offices in the US and Europe, we’re looking for do-ers, problem-solvers, and entrepreneurs like you who want to make an impact in our industry, grow with us, and create a community where everyone thrives.
Careers mean more at Gen II – more opportunity, more innovation, and more ideas! You’ll have the opportunity to grow in ways that are meaningful to you and work alongside some of the best people in their field. You’ll also receive training which will advance your skill set, both technically and personally allowing you to achieve your career aspirations with us. Be part of our community that recognizes achievements, promotes from within, and receive meaningful benefits which focus on your physical, mental and financial well-being.
www.gen2fund.com
Position Overview We are in the midst of a significant technology and AI-led transformation, moving from a traditional services model to an AI-native, platform-first operating company.
This role sits at the heart of that transformation. The role will drive tangible impact across multiple workstreams — from process re-engineering and strategic delivery through to the practical application of AI and automation across operations. This is not a strategy-on-paper role. It’s about doing the work, moving things forward, and making measurable change happen.
You’ll report directly to the Chief Transformation Officer and work closely with technology, operations, data, and commercial leadership across all regions.
Key Responsibilities Process & Operational Improvement Working with selected partners:
- Own and drive the enterprise process improvement agenda adopting an AI / Tech First approach — identifying, prioritizing, and delivering changes that reduce cost, improve quality, or accelerate delivery
- Lead a team of BA’s to map, challenge, and redesign end-to-end workflows across fund operations by priority – e.g., onboarding, client services and other service lines
- Partner with platform and project teams to ensure that they are adopting a AI / Tech first approach to streamline processes while maintaining adequate levels of control in line with our SOC controls and regulatory requirements
- Create and maintain a process catalogue as a living asset that underpins automation and AI deployment decisions & prioritization
- Work with tech, platform and operational teams to quantify inefficiencies, agree benefits and to drive the adoption of new ways of working
Strategy & Delivery
- Working alongside strategic project team and platform owners to support the translation of strategic priorities into structured delivery plans
- Challenge the ‘status quo’ driving a culture of continuous improvement across CX, Productivity and Service Quality.
- Design and present target state processes, highlighting the benefits the changes will bring and introducing appropriate KPIs that evidence this impact
AI Integration & Enablement
- Support the identification, qualification and prioritization of AI use cases across the business, working with the AI Committee and technology teams to move from concept to deployment
- Support the governance framework for AI adoption, including use case approval processes, risk assessment, and benefit tracking
- Champion practical AI adoption across operational teams — driving awareness, building confidence, and ensuring tools are used effectively
- Monitor emerging AI capabilities and assess their applicability to fund administration workflows
- Track and report on AI deployment outcomes, ensuring initiatives deliver against stated ROI and operational targets
- Collaborate with technology and data leadership to create and maintain data ontologies, taxonomies and product catalogues as encoded assets that underpin agentic AI and process automation.
- Collaborate with technology and data leadership to design and build AI-ready integrations needed to support secure, safe, auditable and insightful AI access to enterprise data assets.
- Participate in governance frameworks for enterprise data, ensuring data products and architecture design decisions will support an AI-native, platform-first operating company.
Essential Required Qualifications
- Proven track record of driving process improvement and strategic delivery in a complex, regulated financial services environment
- Strong understanding of operational workflows — ideally within fund administration, asset management, or PE/alternative investments
- Demonstrable experience working with or deploying AI / automation tools in an enterprise setting (not just theoretical interest)
- Comfortable operating at both strategic and tactical levels — equally capable of presenting to a board and rolling up sleeves to get something shipped
- Excellent stakeholder management across senior leadership, operations, and technology functions
- Strong analytical and communication skills — able to turn complex problems into clear recommendations and actionable plans
- Self-starter with high accountability — takes ownership, follows through, and doesn’t wait to be told what to do
Desirable
- Experience with enterprise AI platforms (e.g., Claude Enterprise, document intelligence tools, workflow automation)
- Background in management consulting, transformation, or operational excellence within financial services
- Familiarity with fund administration platforms and ecosystems
Why This Role
- Direct impact — you’ll work at the centre of a transformation that is genuinely reshaping how a global financial services business operates
- Breadth — process, strategy, AI, vendor management, board engagement — no two weeks will look the same
- Autonomy — you’ll own your workstreams with real decision-making authority, reporting directly to the CTrO
- Growth — this is a platform role for someone who wants to build a career at the intersection of operations, technology, and AI in private equity
The salary range for this position is $260,000 - $300,000, in addition to a discretionary bonus and comprehensive benefits package. Please note that the actual salary offered within that range will depend on the candidate’s experience level. Work Arrangement All applicants applying to Gen II Fund Services, LLC must be legally authorized to work in the United States. Please note that all US employees are required to work a hybrid schedule, comprised of three (3) days a week in our office and two (2) days remotely.
https://gen2fund.com/candidate-privacy-statement/
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Gen II Fund Services, 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 $211,950 based on 13,048 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 $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Gen II Fund Services AI Hiring
Gen II Fund Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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