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About This Role
Sequoia Financial Group is a growing Registered Investment Advisor (RIA), headquartered in Northeast Ohio, offering financial planning and wealth management services. At Sequoia, we exist with a singular purpose: to enrich lives. Our values define how we behave and guide us through the pursuit of our purpose to enrich lives. At Sequoia, our core values are:
- + Integrity. We act in the best interests of others by providing an honest, consistent experience for our clients and team.
+ Passion. We pursue our full potential, seeking to continually enhance and evolve our ability to serve our clients and team.
+ Teamwork. We subordinate our egos to work together for the benefit of our clients.
Our promise to team members is that you will grow with us. From experienced advisors to new college grads to transitioning principals, every team member will find Sequoia a place to refine their professional mission, move into new opportunities, go deeper, and lead further. We are built to help you build a career here as a long\-term contributor in our work to enrich lives for generations. Summary of the position
The AI Discovery Specialist is a strategic and hands\-on role responsible for leading enterprise\-wide AI discovery to identify, frame, and prioritize high\-impact business problems that can be solved with AI, LLMs, and Data Science. This individual will analyze and optimize end\-to\-end business processes, build rapid proofs of concept, and drive automation initiatives that enhance personalization, operational efficiency, and strategic decision\-making across Sequoia Financial Group.
This role sits at the intersection of AI/ML innovation, process optimization, rapid prototyping, and cross\-department collaboration — ensuring that AI\-first solutions are discovered, validated, and iterated to deliver clear, quantifiable value aligned with Sequoia's mission to redefine the client experience through data\-driven insights and innovative technology.
Responsibilities
- Lead enterprise\-wide AI discovery to identify, frame, and prioritize high\-impact business problems solvable with AI, LLMs, and Data Science, with clear, quantifiable value
- Conduct user interviews, field observations, and journey walkthroughs to surface real problems behind stated requests; apply Jobs\-To\-Be\-Done and behavioral insights
- Create a Business Case Repository with ROI and Value Metrics to justify and rank use cases for AI\-first Transformation
- Convert qualitative insights into quantified problem definitions with baselines, success metrics, constraints, and value hypotheses
- Build opportunity canvases and prioritization frameworks (impact, effort, risk, time\-to\-value) to focus on the most valuable use cases
- Map current\-state processes, identify pain points, and quantify improvement opportunities (cycle time, throughput, quality, cost)
- Design future\-state workflows considering AI/LLM augmentation, straight\-through processing, and exception handling
- Manage intake for new processes, automation requests, and optimization initiatives
- Coordinate discovery workshops, kaizen events, and lessons\-learned sessions with stakeholders
- Design and build POCs using LLMs, retrieval\-augmented generation (RAG), prompt engineering, RPA, and classical ML to validate feasibility quickly
- Evolve POCs into MVPs with clear functional boundaries, guardrails, and success criteria; prepare handoffs for engineering or vendor buildout
- Run short discovery sprints with user testing, A/B or champion–challenger evaluations, and weekly feedback loops
- Maintain a transparent backlog and roadmap; time\-box experiments and enforce clear "continue/pivot/stop" gates
- Partner with leaders in Client Experience, Operations, Compliance, Planning, and Marketing to stand up shared automation and optimization initiatives
- Align process definitions and business rules with the Data Dictionary and Data Governance artifacts to ensure consistency
- Embed responsible\-AI principles, data privacy, RBAC, and human\-in\-the\-loop controls; escalate model/LLM limitations and biases with mitigations
- Coordinate with Legal/Compliance on acceptable use, content controls, and auditability
- Create decision playbooks, process guides, and workflow aids that make AI outputs usable by front\-line teams
- Lead show\-and\-tell sessions and office hours to grow an optimization culture and improve intake quality
- Coordinate with vendors and internal engineering on process automation options, integration approaches, and build\-vs\-buy decisions
- Align deliverables with PMO milestones, capacity constraints, and Definition of Done
Required Skills/Experience
- Bachelor's degree required; Master's degree preferred
- 8–12 years in AI/ML, process optimization, automation, analytics, or a similar problem\-solving role
- Demonstrated passion for process improvement and human\-centered discovery; ability to translate ambiguous needs into quantified, testable hypotheses
- Hands\-on experience building POCs with LLMs (prompt engineering, evaluation, RAG) and ML models in Python; comfort with notebooks, version control, and experiment tracking
- Proficiency in business process modeling, value stream mapping, and workflow optimization techniques (Lean, Six Sigma)
- Strong communication skills with the ability to dive deep with users and influence executives using crisp narratives and visuals
- Familiarity with Data Governance concepts, Data Dictionary, and privacy/RBAC guardrails
- Experience working in iterative, product\-centric delivery models with short feedback cycles
Preferred Skills/Experience
- Background in financial services/wealth advisory and familiarity with Salesforce and planning/portfolio platforms (e.g., Tamarac, Orion, Addepar, Black Diamond); exposure to eMoney, custodians, and Box is a plus
- Practical knowledge of LLM tooling (prompt templates, vector stores, retrieval pipelines), RPA platforms, classical ML, and evaluation frameworks
- Comfort with BI and communication via Tableau/Power BI for decision enablement
- Experience facilitating workshops, design sprints, or JTBD interviews
Competencies
- Discovery Mindset — Surfaces real problems behind stated requests using human\-centered methods and behavioral insights
- Analytical Rigor — Converts qualitative insights into quantified problem definitions with baselines, success metrics, and value hypotheses
- Rapid Experimentation — Builds POCs and MVPs quickly, enforcing clear continue/pivot/stop gates to de\-risk solutions before scale
- Collaboration — Partners across Client Experience, Operations, Compliance, Planning, Marketing, and vendor stakeholders to drive shared outcomes
- Accountability — Owns the end\-to\-end journey from idea POC MVP with measurable business value
- Adaptability — Thrives in experimentation, iterative development, and fast\-moving AI landscapes; comfortable learning from failed experiments and pivoting quickly
Salary Context
This $135K-$158K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Sequoia Financial Group, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($146K) sits 18% below the category median. Disclosed range: $135K to $158K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Sequoia Financial Group AI Hiring
Sequoia Financial Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dublin, OH, US. Compensation range: $158K - $158K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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