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About This Role
The Wealth Management Operations Division is seeking an experienced Vice President to lead the Applied AI Program and low code initiatives, driving the strategic convergence of advanced AI capabilities and cross\-functional analytics to deliver measurable operational, risk, and experience improvements across the division.
You will be responsible for enhancing operational performance through the widespread use of advanced engineered prompts within the firm's out\-of\-the\-box AI solutions, while simultaneously leading and evolving the division's business intelligence function — encompassing data lifecycle management, analytics strategy, and the exploitation of low\-code tools to create business value.
You will design, develop, and deploy sophisticated human\-in\-the\-loop "AI Assistants" through a controlled, risk\-averse lifecycle — from ideation and prompt engineering through piloting, governance review, and scaled production rollout. In parallel, you will enhance and establish cross\-functional analytics initiatives, address developments in key strategic data architecture, and ensure BI capabilities are tightly aligned with the Wealth Management Division's target operating model.
You will collaborate with business stakeholders, Operations core BI teams, Technology, Risk \& Compliance, and senior leadership to define unified roadmaps, prioritize initiatives, and implement scalable AI and BI solutions aligned with organizational goals.
We are looking for a seasoned professional with a minimum of 7 years of progressive experience in a fast\-paced, complex environment, evidenced by a proven track record spanning applied AI solution design, prompt engineering strategy, responsible AI deployment, and business intelligence program leadership.
Core Responsibilities
Applied AI Program Leadership
- Define and lead the Applied AI Program vision and strategic roadmap, aligning AI Assistant development priorities with division\-wide operational, risk, and experience objectives.
- Drive widespread adoption of advanced engineered prompts within the firm's approved AI platforms (e.g., enterprise copilots, LLM\-based tools), establishing best practices, prompt libraries, and reusable templates that maximize out\-of\-the\-box solution value.
- Design and deliver sophisticated human\-in\-the\-loop AI Assistants that augment employee and client\-facing workflows, ensuring appropriate human oversight, escalation paths, and feedback loops are embedded by design.
- Establish and enforce a controlled, risk\-averse AI solution lifecycle, encompassing use case intake, feasibility assessment, prompt engineering, testing \& validation, risk review, pilot deployment, performance monitoring, and production scaling.
- Continuously modernize the division's applied AI approach by monitoring emerging AI patterns (e.g., agentic workflows, retrieval\-augmented generation, multi\-modal prompting), assessing fit\-for\-purpose use cases, and driving implementation playbooks from pilot to production.
- Lead and evolve the Wealth Management Division's Business Intelligence function, enhancing and establishing cross\-functional analytics initiatives that deliver actionable insights to business stakeholders.
- Own the data lifecycle management strategy, ensuring data quality, lineage, governance, and accessibility standards are maintained across all BI assets and reporting platforms.
- Exploit low\-code and self\-service analytics tools to democratize data access, accelerate report development, and empower business users to derive value independently.
- Address developments in key strategic data architecture, partnering with Technology to ensure the division's data infrastructure supports current and future analytical and AI workloads.
- Align Operations BI strategy with the Wealth Management Division target operating model, working in close partnership with Operations core BI teams and Technology to harmonize priorities, tools, and methodologies.
- Work closely with business representatives to ensure key decisions and progress are made in line with divisional strategic objectives and operating model requirements.
Cross\-Cutting Responsibilities
- Develop robust business cases for new AI Assistants, BI enhancements, and analytics products, including financial justification (ROI), efficiency gains, risk reduction metrics, and experience improvement KPIs.
- Lead and empower cross\-functional teams — including business analysts, data engineers, prompt engineers, BI developers, UX designers, and subject matter experts — to deliver complex solutions ensuring strategic alignment and successful outcomes.
- Foster strong partnerships with Data \& Analytics, Engineering, Risk \& Compliance, Information Security, PMO, HRIS, and business line leaders to ensure seamless execution and integration across both the AI and BI portfolios.
- Uphold and ensure adherence to governance, data privacy, and regulatory compliance standards across all AI and BI products and initiatives.
- Strategically manage vendor relationships, encompassing platform evaluation, selection, contract negotiation, and ongoing performance oversight for both AI and BI tooling.
- Identify and drive synergies between the Applied AI and BI programs, leveraging BI data assets and analytics capabilities to inform AI Assistant design, and using AI to enhance BI workflows (e.g., natural language querying, automated insight generation, anomaly detection).
Required Qualifications
- Minimum 7 years of progressive experience in applied AI, business intelligence, data analytics, or a related discipline within a fast\-paced, complex environment (financial services or wealth management preferred).
- Proven track record in AI solution design and deployment, including prompt engineering, human\-in\-the\-loop system design, and responsible AI practices.
- Demonstrated experience leading BI teams and analytics programs, including data lifecycle management, reporting platform strategy, and self\-service/low\-code tool adoption.
- Strong understanding of data architecture, data governance, and data quality frameworks.
- Experience building and presenting business cases with ROI analysis and KPI frameworks to senior leadership.
- Exceptional ability to collaborate across business, technology, risk, and operations teams in a matrixed organization.
- Familiarity with enterprise AI platforms (e.g., Microsoft Copilot, OpenAI enterprise solutions) and BI platforms (e.g., Tableau, Power BI, Alteryx, or similar).
- Strong knowledge of governance, data privacy, and regulatory compliance requirements relevant to financial services.
Preferred Qualifications
- Experience in Wealth Management or Financial Services operations and technology.
- Hands\-on experience with LLM\-based applications, retrieval\-augmented generation (RAG), and agentic AI frameworks.
- Familiarity with low\-code/no\-code development platforms and their application in analytics and workflow automation.
We Offer Best\-In\-Class Benefits
Healthcare \& Medical Insurance
We offer a wide range of health and welfare programs that vary depending on office location. These generally include medical, dental, short\-term disability, long\-term disability, life, accidental death, labor accident and business travel accident insurance.
Holiday \& Vacation Policies
We offer competitive vacation policies based on employee level and office location. We promote time off from work to recharge by providing generous vacation entitlements and a minimum of three weeks expected vacation usage each year.
Financial Wellness \& Retirement
We assist employees in saving and planning for retirement, offer financial support for higher education, and provide a number of benefits to help employees prepare for the unexpected. We offer live financial education and content on a variety of topics to address the spectrum of employees’ priorities.
Health Services
We offer a medical advocacy service for employees and family members facing critical health situations, and counseling and referral services through the Employee Assistance Program (EAP). We provide Global Medical, Security and Travel Assistance and a Workplace Ergonomics Program. We also offer state\-of\-the\-art on\-site health centers in certain offices.
Fitness
To encourage employees to live a healthy and active lifestyle, some of our offices feature on\-site fitness centers. For eligible employees we typically reimburse fees paid for a fitness club membership or activity (up to a pre\-approved amount).
Child Care \& Family Care
We offer on\-site child care centers that provide full\-time and emergency back\-up care, as well as mother and baby rooms and homework rooms. In every office, we provide advice and counseling services, expectant parent resources and transitional programs for parents returning from parental leave. Adoption, surrogacy, egg donation and egg retrieval stipends are also available.
Benefits at Goldman Sachs
Read more about the full suite of class\-leading benefits our firm has to offer.
Opportunity Overview
CORPORATE TITLEVice President
OFFICE LOCATION(S)New York
JOB FUNCTIONData Analytics \& Reporting
DIVISIONAsset \& Wealth Management
SALARY RANGEUSD 100,000 \- 235,000
Salary Context
This $100K-$235K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Goldman Sachs, 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($167K) sits 23% below the category median. Disclosed range: $100K to $235K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Goldman Sachs AI Hiring
Goldman Sachs has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span New York, NY, US, Richardson, TX, US. Compensation range: $235K - $250K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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