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About This Role
Job Description
Senior Solution Architect \- Data, Analytics and AI
Job Location: Dallas, Texas
Location Flexibility: Multiple Locations in Country
Req Id: 9163
Posting Start Date: 7/1/26
At Fujitsu, we've been driven to create a sustainable world through innovation since 1935\. Today, we lead in digital transformation globally with our 130,000 employees across 50\+ countries. We empower our diverse community to achieve greatness through career development and opportunities. Explore our internal positions and join us in shaping a brighter future. Thank you for being a part of Fujitsu. We look forward to growing together toward a brighter future.
We are seeking a Senior AI Solutions Architect to join Fujitsu’s Data and AI team in North America.
This hybrid role combines strategic solution architecture with technical presales and customer success skills. You will work directly with customers to identify opportunities, design AI\-driven solutions, and deliver production\-ready systems that solve real\-world business challenges across industries such as manufacturing, healthcare, utilities, retail, and financial services.
- Workstyle: Remote \- Candidates may be based anywhere in the US or Canada with ability to travel throught North America as/when needed
- Fulltime \- Consultants will become fulltime/regular employees of Fujitsu North America's Consulting Team
- Classification: This job posting is to fill an existing vacancy within our organization.
Scope :
Applicants should have a successful track record of creating innovative data analytics, machine learning, decision intelligence and data governance solutions to satisfy use cases and business challenges across industry sectors. The applicants must demonstrate proficiency with cloud service provider platforms, open\-source technology platforms, and expertise with a variety of emerging data technologies such as artificial intelligence, computer vision, decision intelligence, structured and unstructured analytics, and augmented data management (including metadata and data governance). Experience with visualization and dashboard creation is valuable but is not sufficient to demonstrate the thought leadership required in this role.
This is a customer\-facing role that demands strong problem solving, articulation and interpersonal skills. You will be responsible for opportunity assessment, solution roadmap, design, risk management, and pricing of opportunities. You will own technical pre\-sales support, solution development, integration and transition to operations. You will collaborate with program / transition managers to transition the solution design to engineering and technical delivery teams. You will be confidently able to communicate with and influence key customer decisions makers such as IT Directors, program managers and partners. The role is expected to carry financial and non\-financial performance targets.
You will exhibit visionary leadership to evangelize global data, analytics and AI solutions. You will actively demonstrate leadership in North American sales pursuit opportunities, solution architecture, and offering development by applying a broad understanding of technology and business solutions.
You will demonstrate aptitude, pragmatism, self\-initiative, and the ability to gain expertise on new technology and solutions with minimal training or overview. You will work under general direction within a clear framework of accountability. You will exercise personal responsibility and autonomy. You will plan your own work and meet given objectives and processes. You will influence direction of teams and specialist peers internally. You will create and apply consistent standards, methods, and tools. You will demonstrate an analytical and systematic approach to problem\-solving. You will communicate orally and in writing fluently and concisely. You will effectively present complex technical information to both technical and non\-technical audiences to enable collaboration between stakeholders.
The definition of success for this role is creating innovative data, analytics and AI solutions that deliver high value to our clients and that exceed our clients’ expectations.
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What You’ll Do
Client Engagement \& Problem Solving
- Partner with clients and stakeholders to understand business processes, identify opportunities, and define AI\-driven solutions
- Act as a trusted technical advisor, translating complex business needs into scalable data and AI architectures
- Lead client\-facing workshops, presentations, demos, and proof\-of\-concept engagements
Clearly communicate technical concepts, progress, and outcomes to both technical and non\-technical audiences
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Solution Architecture \& Technical Leadership
- Design end\-to\-end data, analytics, and AI/ML solutions across cloud and hybrid environments
- Define data architectures including data lakes, warehouses, and real\-time processing systems
- Select and apply appropriate tools, frameworks, and best practices for each engagement
- Lead technical design and guide implementation across multiple projects
- Design and develop machine learning and generative AI solutions using modern frameworks (e.g., PyTorch, scikit\-learn, LLM ecosystems)
- Work with big data and distributed systems (e.g., Spark, Kafka, Hadoop ecosystem)
- Design/ develop cloud\-native solutions using Azure and/or AWS, including serverless and event\-driven architectures
Contribute to reusable assets, accelerators, and internal best practices
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Collaboration \& Delivery Excellence
- Work in agile, cross\-functional teams including data scientists, engineers, and developers
- Contribute to a collaborative, knowledge\-sharing team environment
- Manage multiple pursuits and priorities in a fast\-paced setting
- Ensure high\-quality, production\-ready deliverables and strong client satisfaction
Technical Competences
Must have:
- Extensive experience (10\+ Yrs.) in designing end\-to\-end data, analytics and AI solutions from ingestion, ETL, storage retrieval, integration, processing, analysis, and presentation.
- Demonstrated experience in designing Machine Learning, Data Science, GenAI, and Agentic AI solutions including the use of supervised and unsupervised learning models/algorithms. MCP, RAG, VLM, and multi\-agent architecture.
- Demonstrated expertise creating and presenting solutions to customers in a pre\-sales, technical sales support, and/or technical offering role
- Extensive experience defining and documenting data architecture, relational and non\-relational data modeling (conceptual, logical, and physical) within varying business domains and scenarios
- Extensive experience with relational databases like Oracle or Microsoft SQL Server and with NoSQL databases like MongoDB, Cassandra
- Hands\-on experience designing end\-to\-end data ETL pipelines and processes
- Experience with defining data security architecture and standards, encryption at rest, and transit
- Experience in the design and development of master data management and data governance solutions
- Experience in defining modern data architecture on public and hybrid clouds like Azure, AWS, OpenShift
- Experience with real\-time streaming methodologies like pub\-sub, queues
- Experience with big data technologies and frameworks like Hadoop, HDFS, Kafka, Spark, MLops
- Experience with analytics and business intelligence platforms that provide the ability to design self\-service analytics, dashboards, and cubes
- Technical proficiency in database languages like SQL, PL/SQL, T\-SQL
- Technical proficiency in object\-oriented programming languages like C\#, Java
- Technical proficiency in scripting languages like Python, JavaScript
Experience in User Interface and User Experience design
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Good to have:
- Experience with IoT solutions and integration solutions
- Experience in CI/CD DevOps for data analytics solutions
- Awareness and familiarity with application transformation technologies as Kubernetes, Docker, and Pivotal Cloud Foundry
- Experience in building solutions based on microservice / serverless architecture
Experience in building web services using RESTful framework
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Soft Skills
- Developing consensus\-based solutions with internal and customer architects
- Creating professional design documents and presenting the solution to stakeholders
- Ability to articulate pros \& cons of design decisions, accept corrections and direct the team to rapidly conclude the best possible solution
- Communicating orally, in writing, and in presentation to deliver a concise, thoughtful, and powerful message
- Initiates customer engagement and demonstrates thought leadership
- Maintaining and enforcing compliance with corporate and client enterprise and data security standards
- Proactively identifying process improvement opportunities, challenging conventional practices, and adopting new methods and best practices.
- Demonstrating leadership skills and the ability to interact and lead people in a matrixed organization – tact and diplomacy are essential attributes
- Leading and working with on\-shore and off\-shore teams to achieve project goals and milestones
- Understanding of commercial and financial aspects of creating customer solutions
Ability to work under pressure and demonstrably action orientated
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Qualifications
- Bachelor’s degree in Computer Science or a related field, or equivalent work experience.
- Minimum 10 years of Data/ ML engineer and solution experience, ideally in a global environment.
- A minimum of 3 years of pre\-sales or technical sales support experience
Mobility and Conditions
(Travel, work environment, physical demands, certificates, licenses, etc.)
- Legally entitled to work in North America (US or Canada), with an ability to travel throughout both regions as/when needed. (Some global travel to other regions may at some point be required).
Language: Fluent in English (speaking / writing). French for Canada would be an asset.
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Fujitsu is the leading Japanese information and communication Technology Company and a leading provider of IT products and services including hardware, software, networking and business solutions to customers in more than 100 countries. We are known for our ability to harness the power of IT for our clients and to build innovative practices and solutions. At Fujitsu, you will find a dynamic work environment with multidisciplinary teams involved in stimulating projects. Fujitsu is where you can pursue your full career potential. We enable our employees to grow as individuals, mature as industry professionals and proudly succeed as key members of the Fujitsu team.
At Fujitsu, one of our corporate principles is "We respect human rights". This principle underpins all our corporate and individual activities and guides the actions of every member of the Fujitsu Group. We embrace diversity and equal opportunity. Qualified candidates will be considered for employment without regard to race, color, religion, gender, gender identity or orientation, sexual orientation, national origin, genetics, disability, age or veteran status. By empowering people, we can unleash our collective strengths to create a better experience for our employees, customers, and partners.
As we are looking for a remote position, the disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. Fujitsu aligns salaries 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 $152,400\.00 to $213,360\. Additionally, this role will be eligible for a short\-term incentive based on company results and individual performance.As a technology company, Fujitsu recognizes that human resources are its most important capital. To create an environment where all employees can work positively and healthily, both in mind and body, we offer a full range of health, financial savings plans, and other benefits.
*Note* : While our professional resourcing team is responsible for the interviewing and selection of qualified candidates, Fujitsu may use Artificial Intelligence (AI) tools to support in the screening, shortlisting, or preliminary assessment of applications
Relocation Supported: No
Visa Sponsorship Approved: No
At Fujitsu, we are committed to an inclusive recruitment process that values the diverse backgrounds and experiences of all applicants. We believe that hiring people from a wide variety of backgrounds makes us stronger, not because it's the right thing to do, but because it allows us to draw on a wider range of perspectives and life experiences.
Salary Context
This $152K-$213K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($182K) sits 16% below the category median. Disclosed range: $152K to $213K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Fujitsu AI Hiring
Fujitsu has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Ontario, CA, US, Dallas, TX, US. Compensation range: $96K - $213K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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