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Job Description
Managing Consultant \- Data \& AI (Wayfinders Consulting)
Job Location: New York City, New York, New Jersey, New York, New York, New York, New York City. New York, New York. New York
Location Flexibility: Multiple Locations in Country
Req Id: 11130
Posting Start Date: 8/7/26
At Fujitsu, we're on a mission to create a more sustainable world by building trust in society through innovation. Since our inception in Japan in 1935, Fujitsu has consistently been at the forefront of technological advancement. Today, we stand as a global leader in digital transformation, dedicated to reshaping businesses and society in the digital age.
What truly sets us apart is our family of nearly 130,000 dedicated employees that spans over 50 countries, forming a diverse and dynamic community. We are committed to helping our employees grow and develop their careers. We believe that everyone has the potential to achieve great things, and we are dedicated to providing the resources and opportunities that our employees need to succeed.
We invite you to take the next step in your career journey and apply. Thank you for being a part of Fujitsu. We look forward to growing together toward a brighter future.
Wayfinders is Fujitsu’s new business and technology management consulting firm built for the AI era. The mission of Wayfinders is to help customers define how they should harness the power of technology to outperform their peers and drive new frontiers of growth and profitability. We help customers from opportunity to outcome; we work with them to develop their strategy, implement it at scale, and maximize their return on investment. We do all this by weaving AI into our consulting process and building our customers’ capabilities so they can leverage AI at scale across their enterprise.
Position Overview: Wayfinders Managing Consultant – Data \& AI
Managing Consultants are senior delivery leaders and AI system owners responsible for end\-to\-end solution architecture, engineering rigor, and client success. They ensure AI\-first systems are designed, built, deployed, and governed to operate reliably at enterprise scale.
This role combines deep technical judgment with consulting leadership and accountability.
Key Responsibilities
AI Architecture \& System Ownership
- Define end\-to\-end AI architectures across data, models, agents, orchestration, and execution
- Determine when to use rules, ML, LLMs, or hybrid approaches
- Design systems for scalability, reliability, and security
Engineering, MLOps \& Lifecycle Leadership
- Establish engineering standards, CI/CD patterns, and testing strategies
- Oversee deployment, monitoring, retraining, and evolution of AI systems
- Ensure data quality, model performance, and decision integrity
- Lead Responsible AI, governance, and risk mitigation efforts
Platform \& Integration Leadership
- Make platform tradeoff decisions (e.g., Databricks vs. Snowflake vs. Palantir)
- Integrate AI systems with enterprise platforms (ERP, CX, supply chain)
- Define reusable reference architectures and accelerators
Client \& Practice Leadership
- Act as a trusted advisor to senior client stakeholders
- Lead teams across Wayfinders and delivery organizations
- Contribute to proposal development, offering design, and IP creation
- Mentor Senior Consultants and Consultants
Required Skills \& Experience
Experience \& Education
- 4–7 years of experience in consulting or equivalent industry roles
- Proven leadership of AI, data, or advanced analytics initiatives
- Bachelor’s degree required; advanced degree preferred
Technical \& Architectural Expertise
- Deep experience with cloud\-native data and AI platforms
- ML, LLMs, agentic systems, and decision workflows
- MLOps, monitoring, governance, and Responsible AI
- Strong architectural tradeoff and system\-design skills
Leadership Competencies
- Executive communication and client leadership
- Team leadership and coaching
- Strategic thinking with delivery accountability
Fujitsu salaries are aligned to the specific geographic location in which the work is primarily performed. It is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the circumstances of each situation. The pay range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to: specific skills, qualifications, experience, and comparison to other employees already in this role. The pay range for this position is estimated at $140,000 to $180,000\. Additionally, this role may be eligible for a short\-term incentive based on company results and individual performance.
Relocation Supported: No
Visa Sponsorship Approved: No
At Fujitsu, we are committed to creating a diverse and inclusive workplace where everyone feels valued and respected. We believe that diversity and inclusion are essential to our success, and we are committed to creating an environment where all employees can thrive.
We are an equal opportunity employer and qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, marital status, sexual orientation, gender identity or expression, genetic information, veteran status, or any other characteristic protected by law.
We believe that everyone has something to contribute, and we are committed to creating a workplace where everyone can reach their full potential.
California Consumer Privacy Act (CCPA), read here
Salary Context
This $140K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Fujitsu, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($160K) sits 26% below the category median. Disclosed range: $140K to $180K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Fujitsu AI Hiring
Fujitsu has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span San Jose, CA, US, New York, NY, US, Dallas, TX, US. Compensation range: $180K - $180K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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