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About This Role
InvestorFlow is the only company of its kind to deliver simple and smart CRM and digital portals for deals and investors to streamline every customer interaction. Asset managers use our platform to easily make transactions with increased productivity and transparency.
InvestorFlow is building a dedicated AI Consulting Practice within Professional Services to help private markets firms design, adopt, and operationalize AI across their front, mid and back office functions. This is a greenfield opportunity for an exceptional executive who combines deep management consulting credentials, genuine AI expertise, and the commercial instincts needed to build and grow a high-impact practice.
The Managing Director of AI Services will serve to help develop, grow and set the AI strategy, as well as running engagements, developing intellectual property, and partnering closely with Sales to originate and close new business. This role carries both a delivery mandate and a revenue mandate, making it equally suited to a consulting leader who is comfortable in a client-facing sales environment as it is to a revenue-driven executive with serious consulting depth
You Will:
Practice Leadership & Strategy
- Together with the Chief of Global Services, define and execute the vision, go-to-market positioning, and operating model for InvestorFlow’s AI Consulting Practice.
- Establish service offerings, engagement frameworks, pricing structures, and delivery standards.
- Build and develop a high-performance consulting team, including hiring, mentoring, and performance management.
- Collaborating with Product, help develop reusable IP, accelerators, and methodologies that differentiate InvestorFlow’s advisory capabilities and drive repeatability at scale.
- Serve as the internal authority on AI in private markets — advising product, marketing, and executive leadership on market trends and client needs.
Engagement Delivery & Practice Operation
- Serve as executive sponsor and lead practitioner on flagship AI consulting engagements.
- Oversee the end-to-end delivery of all sold engagements, ensuring quality, timeliness, and measurable client outcomes.
- Help build and foster a talented consulting team capable of delivering high quality projects and engagements.
- Guide clients through AI readiness assessments, strategy development, use-case prioritization, and implementation roadmaps.
- Partner with InvestorFlow’s platform teams to align advisory services with product capabilities and roadmap.
- Build and sustain trusted advisor relationships with senior client stakeholders including C-suite and Managing Partners.
- Understanding of AI engineering disciplines, including prompt engineering, model evaluation, retrieval strategies, agent frameworks, vector search, and observability.
Commercial Growth & Sales Partnership
- Collaborate with the Sales and Business Development organization to identify, qualify, and close AI consulting opportunities.
- Participate actively in prospect meetings, executive briefings, and RFP processes — bringing subject matter credibility that accelerates deal velocity.
- Develop compelling proposals, business cases, and Statements of Work that translate client pain points into well-scoped, high-value engagements.
- Contribute to pipeline development through thought leadership, industry events, speaking engagements, and strategic account introductions.
- Partner with Account Management to identify expansion opportunities within the existing InvestorFlow client base.
- Own and report on practice revenue, utilization, gross margin, and growth targets.
Technical Leadership & Market Presence
- Deep understanding of modern AI technologies, including LLMs, RAG, agentic AI, multimodal AI, and AI orchestration frameworks.
- Experience designing enterprise AI solutions that align with business processes, operating models, governance, and technology architecture.
- Ability to evaluate and select AI platforms, models, and tooling while ensuring security, compliance, governance, and responsible AI practices.
- Strong understanding of enterprise data architecture, including governance, data quality, metadata, lineage, and knowledge management.
- Experience building scalable AI data foundations across structured and unstructured data, data platforms, and vector-based architectures.
- Familiarity with cloud platforms (Azure, AWS, GCP), enterprise integrations, APIs, workflow automation, and CRM ecosystems.
- Represent InvestorFlow at key private markets conferences, LP forums, and industry roundtables.
- Develop and publish point-of-view content — whitepapers, case studies, blog posts, and briefings — that build InvestorFlow’s brand as an AI authority in private markets.
Maintain a strong external network across GPs, LPs, technology partners, and peer consulting communities. *
You Have:
Required Experience & Credentials
- 12+ years of progressive consulting experience, including at least 4–6 years in a senior leadership role (Principal, Partner, Managing Director, or equivalent) at a top-tier management consulting firm, boutique advisory, or a technology-led consulting practice.
- Demonstrated expertise in AI strategy, AI adoption, and enterprise AI implementation — with the ability to engage credibly at both the strategic and operational levels.
- Experience working with financial services or investment management clients; direct exposure to private markets (private equity, private credit, real assets, or alternatives) strongly preferred.
- Proven commercial track record: experience driving revenue through origination, proposal development, and client relationship expansion.
- History of building or meaningfully scaling a consulting practice, service line, or center of excellence.
- Exceptional executive communication and presentation skills; ability to command a room with senior client stakeholders and internal leadership alike.
Technical & Domain Knowledge
- Substantive knowledge of AI/ML technologies, including large language models, generative AI applications, and AI-driven workflow automation.
- Familiarity with the private markets front-office — investor relations, fundraising, capital allocation, LP reporting, and deal sourcing workflows.
- Understanding of CRM ecosystems and enterprise data strategy in the context of investment management operations.
- Ability to translate complex AI concepts into clear business value propositions for non-technical stakeholders.
Leadership & Culture
- Builder’s mindset — energized by creating something new, not just managing something established.
- Entrepreneurial orientation: comfortable with ambiguity, resource constraints, and the pace of a high growth technology company.
- Collaborative leader who earns credibility through expertise and delivery, not hierarchy.
- High integrity, low ego, and a genuine commitment to client outcomes.
Education
- Bachelor’s degree required. MBA or advanced degree (Computer Science, Data Science, Finance, or related field) strongly preferred.
InvestorFlow offers a competitive total compensation package commensurate with the seniority of this role, including a base salary, performance-based bonus tied to practice revenue and client success metrics, and equity participation.
Additional benefits include comprehensive health coverage, flexible PTO, and a distributed-first work model.Why InvestorFlow?
- Category-defining platform at the intersection of private markets and AI — a rare greenfield consulting opportunity with institutional backing.
- Direct access to a blue-chip LP and GP client base spanning the largest private markets firms in the world.
- Entrepreneurial environment with the resources and executive support to build a practice that matters.
- A mission-driven team that believes AI should make financial markets more transparent, efficient, and accessible.
InvestorFlow is an investor and deal engagement platform that helps investment banks, alternative asset managers, fund administrators, and wealth managers create intelligent digital experiences and dramatically increase productivity and engagement. InvestorFlow is the only solution provider to integrate deal flow management, fundraising, reporting and investor services into one cloud-native platform. InvestorFlow serves over 175 clients including 25 of the top 50 alternative asset managers, with over $3.5 trillion of assets, 750 funds and 90,000 LPs on its combined platform. InvestorFlow is a privately held company based in San Francisco, California. To learn more, please visit www.investorflow.com
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $240K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At InvestorFlow, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($270K) sits 27% above the category median. Disclosed range: $240K to $300K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
InvestorFlow AI Hiring
InvestorFlow has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $300K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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