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About This Role
About SaxeCap
SaxeCap is the premier AI transformation and investment platform in private equity. We pioneered the concept of buying out traditional businesses and transforming them with AI in 2018, and today we are the go-to AI transformation partner for 70+ PE firms globally, including 9 of the 10 largest by AUM as well as the world's leading sovereign wealth funds. We've transformed 200+ businesses across healthcare services, business services, financial services, software, consumer, and more, driving billions of dollars of enterprise value expansion along the way.
We operate at the intersection of three businesses: (1) a private equity investment arm, (2) an in-house AI transformation firm that designs and deploys bespoke GenAI, ML, and automation solutions, and (3) a suite of proprietary software products. Our team comes from places like Google, Palantir, Meta, Surge AI, Blackstone, TPG, Cerberus, Millennium, Balyasny, McKinsey, Bain, Oliver Wyman, MIT, Stanford, IIT, GSB, HBS, and Wharton. Our leadership includes early investors in OpenAI, Anthropic, Groq, SpaceX, Neuralink, Mercor, Lambda Labs, etc. But what makes our team exceptional isn't where they came from or what they've invested in, it's what they ship. We are builders.
With established teams across North America, South America, India, and the United Kingdom, we are now expanding across Asia and continental Europe. Singapore will be our hub for Southeast Asia, and this role is the first hire in that hub.
About the Role
We are hiring a Vice President, AI Deployment & Strategy to join our AI transformation team and establish our presence in Singapore and Southeast Asia, working with global and regional sponsors and the portfolio companies they own across the region. This is not a traditional consulting or investment role. You will own the full lifecycle of AI-driven value creation across our PE partner portfolio, from first engagement with a sponsor, through deep technical and strategic analysis, to managing our engineers and data scientists as they build and deploy solutions that move EBITDA.
You will operate across a range of workstreams simultaneously: evaluating AI opportunities for PE sponsors under deal timelines, designing comprehensive transformation roadmaps for portfolio companies, managing technical teams through production AI deployments, supporting investment underwriting for SaxeCap's own PE vehicle, and leading business development with new partners. The common thread: you are the person who connects AI's technical potential to measurable business value, and you own the outcome end to end.
In Singapore specifically, you will anchor our Southeast Asia presence. The region's sponsors and sovereign investors are among the most sophisticated in the world, and their portfolio companies span a wide range of markets, languages, and regulatory regimes, from Singapore and Malaysia to Indonesia, Vietnam, Thailand, and the Philippines. You will translate SaxeCap's playbook across that diversity: building conviction on AI opportunities in businesses that often operate across several countries at once, managing deployments with distributed teams, and building the relationships with regional management teams that make a transformation durable. You will also help us recruit and develop the team that follows you.
About You
You are a hybrid: part technologist, part strategist, part operator. You are equally comfortable whiteboarding a model architecture with an ML engineer, presenting an AI transformation roadmap to a private equity partner, and pressure-testing an LBO model. You move fast, you think in structures, and you obsess over shipped outcomes rather than theoretical potential. Specifically:
- 5 to 10 years of experience spanning two or more of: private equity, AI/ML product management, strategy consulting (MBB or comparable preferred), investment banking, or engineering
- You have managed teams of 5+ people (engineers, data scientists, consultants, or analysts) and held them accountable to ambitious timelines
- You have managed executive and C-suite stakeholder relationships, ideally in a PE or PE-adjacent context
- You have real AI/ML fluency: you can discuss model architectures, data pipelines, and build-vs-buy trade-offs with technical teams, not just at a buzzword level. Hands-on experience with Python, SQL, and data science tools is expected. Experience shipping ML products, managing RLHF programs, or building GenAI solutions is a strong differentiator.
- You have experience quantifying enterprise value and EBITDA impact of technology or transformation initiatives; not just tracking KPIs, but tying technical outcomes to P&L
- You have product management instincts: understanding user pain points, scoping technical solutions, and managing the build through deployment
- You have operated in fast-paced, ambiguous environments where the scope, stakeholders, and approach shift rapidly, and you thrive in that context rather than resist it
- You have full professional fluency in English; working proficiency in Mandarin, Bahasa Indonesia, Bahasa Melayu, or another major regional language is a strong plus
- You have operated across multiple Southeast Asian markets and understand how business practices, labor models, and data infrastructure differ between them
- You are familiar with the regional regulatory and data environment, including Singapore's PDPA and MAS guidance on AI and data in financial services, and can design AI deployments that respect them
- You are authorized to work in Singapore or are eligible for an Employment Pass
- Bachelor's and/or Master's degree in Computer Science, Engineering, Mathematics, or a related technical field from a top-tier institution
What Sets the Best Candidates Apart
The people who excel at SaxeCap share a specific profile. They are not pure technologists and not pure dealmakers, they are the rare operators who can do both. The strongest signals we look for:
- Shipped AI, not just studied it. You have built or managed the build of AI/ML solutions that went to production and drove measurable business outcomes. "Led AI strategy" without specifics doesn't move the needle. "Shipped an ML model that reduced X by Y%, managing a team of Z engineers" does.
- PE-grade rigor under time pressure. You can form accurate conviction on an AI opportunity from limited information and present that conviction to a PE partner with confidence. If you've worked on a live deal, you know what this feels like.
- Builder DNA. You don't just recommend, you build. Side projects, open-source contributions, 0-to-1 products, or intrapreneurial ventures all signal the instinct we're looking for.
- Structured synthesis at scale. You can process massive amounts of qualitative and quantitative information and distill it into a prioritized plan that senior executives can act on. This is the hardest skill to find and the one we value most.
- Comfort operating across a fragmented region. You have delivered results in businesses that span several Southeast Asian markets simultaneously, each with its own regulations, talent pools, and operating norms, and you know how to design a transformation that holds together across all of them.
Compensation & Growth
SaxeCap offers highly competitive compensation, and you will work directly with our senior leadership from day one, with significant client exposure and rapid professional development. The team is lean by design, there are no layers between you and impact. As our first hire in Singapore, you will have a direct hand in shaping how SaxeCap grows across Southeast Asia and the wider region.
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 SaxeCap, 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.
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.
SaxeCap AI Hiring
SaxeCap has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span London Area, United Kingdom, Tokyo, Japan.
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.
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