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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.
As InvestorFlow builds its AI Consulting Practice, the Senior Consultant, AI Services is a pivotal delivery role at the heart of that effort. This is a practitioner-first position for someone who thrives at the intersection of AI technology and enterprise solutions — a professional who can work hands-on to design and deploy AI-driven solutions, while also communicating clearly with business stakeholders about strategy, process, and outcomes.
The Senior Consultant will deliver high profile client engagements, working closely with other Global Services teams and InvestorFlow's platform and product teams. As the practice matures, this individual will have the opportunity to contribute to practice-building activities — including developing methodology, refining service offerings, and participating in business development conversations alongside senior leadership.
You Will:
Engagement Delivery
- Lead and execute the day-to-day delivery of AI consulting engagements across InvestorFlow's client base, from kickoff through completion.
- Conduct AI readiness assessments — evaluating client data infrastructure, workflows, tooling, and organizational capability to adopt AI solutions.
- Design and document AI use-case inventories, prioritization frameworks, and phased implementation roadmaps tailored to private markets workflows.
- Configure, implement, and validate AI solutions within client environments, including integrations with InvestorFlow's platform and adjacent enterprise systems.
- Manage workstreams, timelines, and deliverables with precision — ensuring engagements stay on scope, on budget, and on schedule.
- Facilitate working sessions, workshops, and stakeholder interviews with both technical teams and business leaders at client organizations.
- Produce high-quality engagement deliverables: current-state assessments, future-state blueprints, implementation playbooks, and executive readouts.
- Identify and escalate risks, blockers, and scope changes proactively, partnering with the MD of AI Services to resolve them.
Technical & Functional AI Implementation
- Translate business requirements into AI solution designs, selecting appropriate approaches across generative AI, LLM-based automation, workflow orchestration, and data integration.
- Hands-on configuration and deployment of AI tools and integrations — including prompt engineering, API connections, agent workflows, and model evaluation.
- Assess and work within client data environments: CRM data quality, data pipelines, structured/unstructured data sources, Snowflake, ERPs, front and mid office tools and systems and governance frameworks.
- Bridge functional and technical workstreams, ensuring that AI solutions are grounded in real operational workflows and that both IT and business stakeholders are aligned.
- Apply knowledge of private markets processes — LP management, fundraising workflows, deal origination, reporting — to contextualize AI recommendations and implementations.
- Test, validate, and iterate on AI-powered outputs to ensure accuracy, reliability, and fitness-for-purpose in a regulated, institutional environment.
Client Relationship & Communication
- Build trusted working relationships with client project teams, serving as a reliable day-to-day point of contact throughout engagements.
- Communicate technical concepts clearly and accessibly to non-technical business stakeholders, including C-suite audiences when needed.
- Gather and synthesize client feedback to continuously improve engagement delivery and solution quality.
- Support senior leadership in steering committee presentations and executive briefings.
Practice Development
- Contribute to the development of reusable engagement accelerators, templates, and methodology documentation as the practice scales.
- Capture and codify engagement learnings, client insights, and implementation patterns into shared IP for the consulting team.
- Participate in pre-sales activities — supporting proposals, scoping conversations, and solution demonstrations alongside the Managing Director and Sales team.
- Contribute to thought leadership content and internal knowledge-sharing as expertise and bandwidth allow.
You Have:
Experience & Background
- 5–8+ years of experience in management consulting, technology consulting, or enterprise AI implementation, with a strong track record of delivering complex, client-facing engagements.
- Direct experience implementing AI, machine learning, or advanced analytics solutions in enterprise environments — not just advising on strategy, but rolling up your sleeves to build and deploy.
- Experience working with financial services, investment management, or private markets clients is strongly preferred.
- Background navigating engagements that span both technical and functional stakeholders — comfortable working with data engineers, CRM administrators, and COOs in the same week.
- Familiarity with project management disciplines: scope management, workplan development, status reporting, and issue/risk tracking.
Technical Skills
- Hands-on proficiency with AI/ML tools and platforms, including large language models (e.g., OpenAI, Anthropic, Azure OpenAI), AI orchestration frameworks, and no-code/low-code AI tooling.
- Working knowledge of prompt engineering, retrieval-augmented generation (RAG), and AI agent workflows.
- Experience with enterprise data environments: CRM platforms (Salesforce, Dynamics, or similar), data integration tools, APIs, and structured/unstructured data handling.
- Proficiency with data analysis and visualization tools (Python, SQL, Excel, Power BI, Tableau, or equivalent) sufficient to support client assessments and solution validation.
- Understanding of data governance, security, and compliance considerations relevant to institutional financial services.
Functional & Domain Knowledge
- Strong grasp of front, mid, and back-office workflows in private markets or investment management — including LP/investor relations, fundraising, deal origination, portfolio monitoring, and reporting.
- Ability to map business processes, identify workflow inefficiencies, and articulate where AI creates measurable operational or commercial value.
- Experience producing enterprise-grade documentation: process maps, functional specifications, solution design documents, and implementation playbooks.
Soft Skills & Attributes
- Exceptional written and verbal communication skills — able to produce polished client-facing deliverables and present with confidence.
- Highly organized, detail-oriented, and able to manage multiple workstreams simultaneously without losing quality.
- Genuinely curious about AI and motivated to stay current on the evolving landscape of enterprise AI tools and use cases.
- Collaborative team player who operates with a low-ego, high-accountability orientation.
- Comfortable with ambiguity and adapts quickly in a fast-moving, high-growth environment.
Education
- Bachelor's degree required in a relevant field (Computer Science, Information Systems, Finance,
Economics, or related). Advanced degree (MBA, MS in Data Science, or equivalent) a plus.
Why InvestorFlow?
- Be a founding member of a new AI consulting practice with genuine opportunity to shape how it grows.
- Work at the intersection of two high-velocity industries: private markets and enterprise AI.
- Direct exposure to world-class blue chip GPs through client delivery.
- A clear path toward practice leadership for high performers, with the Managing Director as a direct mentor and advocate.
- A mission-driven, collaborative culture that invests in its people and operates with integrity.
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 $175K-$200K range is above the median 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($187K) sits 12% below the category median. Disclosed range: $175K to $200K.
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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