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About This Role
About Citco
Citco is a global leader in fund services, corporate governance and related asset services with staff across 50 office locations worldwide. With more than $1.8 trillion in assets under administration, we deliver end-to-end solutions and exceptional service to meet our clients’ needs.
For more information about Citco, please visit www.citco.com
About the Team & Business Line
Fund Administration is Citco’s core business, and our alternative asset and accounting service is one of the industry’s most respected. Our continuous investment in learning and technology solutions means our people are equipped to deliver a seamless client experience.
Strategic Operations is a dedicated function within Private Assets. It brings transformation delivery, project execution, business analysis, operational initiatives and implementation support into one team, reducing the delivery burden on BAU Operations and maintaining ownership from design through adoption.
The group will be split across three distinct focus areas, aligned under two individual leaders:
Operating Model, Product & Client Solutions: led by the Operating Model & Product leader, this area will define how services should run end to end, shape scalable products and solutions, support complex client implementations, and ensure client commitments are operationally deliverable.
Data, Reporting & Platform Enablement: led by the Technology, Data & Delivery leader, this area will define Operations requirements for data, reporting and platforms, strengthen data quality and lineage, and carry solutions through testing, deployment and adoption.
Digital Workflow, Automation & AI: also led by the Technology, Data & Delivery leader, this area will identify and deliver workflow, automation and AI opportunities, establish appropriate controls, and measure whether the expected benefits are realised.
The group works in close partnership with Operations, the Transformation Office, IT, Tactical Operations and Marquee Client teams. Roles may be permanent or approximately 12-month rotational assignments anchored in strategic locations. Rotational colleagues will join from the business, help shape and deliver change, and return to the line with broader project experience.
Your Role:
- You will lead the automation and AI agenda for Private Assets Operations — determining what is worth automating, how it gets deployed, and whether the benefit was real.
- Identify and prioritise automation, digital workflow and AI opportunities across the Private Assets estate.
- Determine the right approach for each opportunity — workflow, automation tooling or AI — and make the case for it.
- Own deployment standards so solutions are reusable and supportable rather than one-off local fixes.
- Partner with IT and the Transformation Office through build, deployment and support.
- Define how benefit will be measured before build starts, and report on whether it was realised.
- Establish the governance and controls around automated and AI-assisted processes, working with Risk and the Control Office.
- Coordinate with Tactical Operations on automation already deployed, and pick up solutions worth scaling.
- Lead and mentor focus area resources, including rotational colleagues joining from the business.
- Report on progress, risks and benefits realization to the relevant leader and senior stakeholders.
About You
- You have 12–15 years’ experience in private assets or a comparable financial services environment, with substantial exposure to automation or digital transformation.
- Proven delivery of automation, digital workflow or AI initiatives that were deployed and adopted, not just piloted.
- Strong technical knowledge of private assets processes — enough to judge what is genuinely automatable and what is not.
- Practical understanding of workflow orchestration, exception-based processing and the control implications of automation.
- Sound commercial judgement on effort versus benefit, and the willingness to stop work that will not pay back.
- Strong leadership skills and supervisory experience of other professionals, including rotational colleagues joining from the business.
- Skilled in business planning, budgeting and stakeholder negotiation.
- Strong written and verbal communication skills.
- Advanced computer skills (Microsoft Excel, Word, PowerPoint and SharePoint).
- Bachelor’s Degree in Accounting, Finance, Economics or a related field; professional accounting designation a plus.
What We Expect of the Strategic Operations Team
- Regardless of role or focus area, everyone in Strategic Operations is expected to demonstrate the following:
- Deep technical and operational knowledge — genuine depth in how our processes, data and platforms actually work — the detail that only comes from having run the work.
- Curiosity — the instinct to ask why a process looks the way it does, to challenge the answer, and to keep asking whether there is a better way.
- A transformational mindset — comfortable redesigning end to end rather than patching, and prepared to see change through past go-live to the point where it is genuinely adopted.
- Ownership through to adoption — accountability does not end at delivery; success is measured by whether the change sticks in the business.
- Commercial and client awareness — an understanding of what we can genuinely run, so that what we commit to a client is deliverable.
- Global mindset — working across regions, jurisdictions and time zones, and designing solutions that scale beyond a single desk, client or location.
- Ambition to shape Citco’s future — an appetite to help build the operating model we will run for years, and to be measured on whether it works.
Salary Range : USD $200,000 - $325,000
- While this role may be posted under a different location, as part of our office transition, the successful candidate's primary work location will be Metropark, located at 170 South Wood Avenue, 4th Floor, Iselin, NJ 08830.
Our Benefits
Your well-being is of paramount importance and central to our success here at Citco. Citco offers a comprehensive and competitive total rewards package to support your career success and personal needs. Your base salary will be determined by several factors such as the role, experience, skillset, market conditions, etc. Furthermore, qualifying positions can participate in an annual discretionary bonus pool based on company profitability and individual contributions. Our comprehensive benefits package includes medical, dental, and vision coverage, short and long-term disability benefits, a retirement savings plan, tuition reimbursement, mental health and wellness support, parental leave, and more. Additional details about our total rewards package will be shared during the hiring process.
At Citco, we take pride in fostering an inclusive culture by prioritizing the hiring of people from diverse backgrounds. Our culture is a source of pride and strength, fostering innovation, mutual respect, and collaboration. We warmly welcome and encourage applications from people with disabilities. If you require any accommodations to make our recruitment process more accessible for you, please let your recruiter know.
As an equal opportunity employer, Citco adheres to making all employment and personnel decisions without discriminating based on race, color, creed, religion, sex, physical disability, mental disability, age, marital status, sexual orientation, citizenship status, national or ethnic origin, or any other protected status. We believe that an inclusive workforce not only enriches our company but also drives us towards greater success.
Please note that this job description is not intended to be all-inclusive. Our employees may perform other job-related duties as needed to meet the ongoing needs of our organization.
Salary Context
This $200K-$325K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Citco, 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. This role's midpoint ($262K) sits 24% above the category median. Disclosed range: $200K to $325K.
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.
Citco AI Hiring
Citco has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Jersey City, NJ, US. Compensation range: $175K - $325K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 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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