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About This Role
Job Description
AI/ML ENG III
Job Location: Dallas, Texas
Location Flexibility: Multiple Locations in Country
Req Id: 11027
Posting Start Date: 8/6/26
About 1FINITY
1Finity, a Fujitsu company, is a global provider of communications networks for our connected world. We uniquely combine technological leadership and expertise in open optical and wireless networking, network automation, and applied AI/ML to design, build, operate, and maintain critical digital communications network infrastructure. Collaborating closely with ecosystem partners, we deliver transformative outcomes for service providers and network operators, and enable them to lower TCO, improve network performance, and increase energy efficiency. We’re also a diverse, inclusive, and innovative workplace that achieves together. We offer highly competitive compensation, benefits, and career development opportunities, as well as flexible options for working your way. See what working at 1Finity looks like at https://www.linkedin.com/company/1finity\-inc/ . For more information, please visit https://1finity.com/?utm\_source\=li\&utm\_medium\=soc.
AI Automation \& Intelligent Workflow Developer III
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Position Summary
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The AI Automation \& Intelligent Workflow Developer III is a senior individual contributor responsible for designing, developing, and deploying enterprise AI automation solutions, intelligent workflows, AI agents, and generative AI applications that improve operational efficiency, decision support, and business effectiveness.
This role focuses on leveraging Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), AI agents, workflow orchestration frameworks, and enterprise system integrations to automate complex business processes, reduce manual effort, streamline decision\-making, and improve organizational productivity.
The ideal candidate combines strong software development and integration expertise with practical experience building intelligent automation solutions that can autonomously gather information, perform analysis, generate content, execute workflows, and deliver actionable business insights. The role partners closely with PMO, Finance, Operations, and IT organizations to transform legacy processes into scalable AI\-enabled solutions.
Key Responsibilities
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### AI Automation \& Intelligent Workflow Development
- Design, develop, and deploy AI agents and agentic workflows that automate complex business processes, reporting activities, decision\-support functions, and operational tasks.
- Build intelligent workflow solutions using orchestration frameworks such as n8n, Microsoft Power Platform, LangChain, LlamaIndex, CrewAI, or similar technologies.
- Develop AI\-enabled solutions capable of autonomous information gathering, data analysis, content generation, workflow execution, and business process automation.
- Design reusable automation architectures that integrate enterprise systems, APIs, databases, reporting platforms, and AI services.
- Evaluate existing business processes and identify automation opportunities that deliver measurable efficiency gains, improved accuracy, and reduced manual effort.
- Implement and maintain Retrieval\-Augmented Generation (RAG) solutions utilizing enterprise knowledge sources and business data repositories.
- Integrate generative AI services into reporting, operational, and administrative workflows to improve productivity and decision\-making.
### Enterprise Reporting \& Decision Support
- Design and enhance executive dashboards, scorecards, performance reporting solutions, and KPI frameworks using Power BI and other reporting platforms.
- Develop AI\-assisted reporting capabilities including automated summaries, intelligent recommendations, insight generation, and data storytelling.
- Create automated reporting pipelines that reduce manual reporting effort while improving consistency and quality.
- Transform enterprise data into actionable information through intelligent automation and AI\-enhanced analytics.
### Data Integration \& Automation Architecture
- Design, develop, and optimize ELT pipelines supporting AI workflows, automation solutions, and enterprise reporting environments.
- Integrate structured and unstructured data from SAP, Microsoft 365, SharePoint, databases, APIs, cloud platforms, and other enterprise applications.
- Develop scalable data architectures supporting AI agents, automation platforms, reporting systems, and knowledge repositories.
- Document solution architecture, integration methods, workflow logic, and operational support procedures.
### AI Governance \& Responsible Deployment
- Establish and maintain standards for AI solution reliability, responsible AI practices, security, governance, and business compliance.
- Develop testing and validation methods to ensure AI\-driven workflows produce accurate, reliable, explainable, and auditable outputs.
- Monitor automation performance and continually optimize workflow effectiveness, reliability, and user adoption.
- Identify risks associated with AI\-generated content, prompt security, data quality, and workflow automation and implement appropriate controls.
### Technical Leadership \& Stakeholder Engagement
- Partner with Finance, PMO, Operations, IT, and business leaders to translate operational challenges into automation and AI solutions.
- Lead technical discussions related to architecture, automation strategy, and AI implementation approaches.
- Provide technical guidance and mentorship to developers, analysts, and business users implementing automation and AI technologies.
- Communicate complex technical concepts in a manner that is understandable to technical and non\-technical audiences.
- Support organizational adoption of AI\-enabled business processes through training, documentation, demonstrations, and knowledge transfer.
Required Qualifications
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### Education
- Bachelor’s degree in Computer Science, Data Science, Information Systems, Artificial Intelligence, Analytics, Engineering, or a related quantitative field required.
- Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, Data Science, or a related field preferred, but not required.
- Equivalent combination of education, relevant certifications, and directly related experience will be considered.
### Professional Experience
- 5\+ years of experience in software development, automation development, enterprise integrations, business intelligence, data engineering, or related technical disciplines.
- 2\+ years of hands\-on experience developing and deploying Generative AI, AI agents, intelligent automation solutions, or agentic workflows in enterprise environments.
- Proven experience implementing automation solutions that deliver measurable business value through improved efficiency, standardization, and reduced manual effort.
- Experience integrating AI capabilities with enterprise systems, reporting environments, workflows, APIs, and business applications.
- Demonstrated ownership of solution design, development, testing, deployment, documentation, and long\-term support of automation initiatives.
Technical Qualifications
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### Artificial Intelligence \& Intelligent Automation
- Hands\-on experience designing, developing, and deploying AI agents, agentic workflows, and Generative AI solutions utilizing Large Language Models (LLMs) and Retrieval\-Augmented Generation (RAG).
- Experience developing intelligent automation solutions using n8n, LangChain, LlamaIndex, CrewAI, Microsoft Copilot Studio, Power Automate, or similar workflow orchestration frameworks.
- Experience creating autonomous or semi\-autonomous workflows that perform business analysis, research, content generation, reporting, decision support, and process execution.
- Familiarity with prompt engineering, context management, tool integration, workflow orchestration, agent memory concepts, and multi\-step agent execution patterns.
- Experience integrating Azure OpenAI, Azure AI Services, OpenAI APIs, Microsoft Copilot technologies, AWS AI services, or comparable AI platforms into enterprise solutions.
- Familiarity with agentic code development using Windsurf, GitHub Copilot, Cursor, or similar AI\-assisted software development environments.
- Knowledge of AI governance, prompt security, output validation, responsible AI practices, and enterprise AI deployment standards.
- Experience monitoring, tuning, and optimizing AI workflow performance, reliability, and business effectiveness.
### Programming, Integration \& Development
- Advanced Python development skills, including experience with APIs, automation scripting, reusable code design, and data processing.
- Strong SQL development experience for data extraction, transformation, validation, and integration.
- Experience integrating enterprise applications, databases, APIs, Microsoft 365 services, SharePoint, cloud platforms, and reporting environments.
- Experience with Git or similar version control tools and structured development practices.
### Business Intelligence \& Reporting Automation
- Strong Power BI development experience, including data modeling, DAX, Power Query, dashboard development, executive reporting, and KPI frameworks.
- Experience automating recurring reports, performance summaries, business reviews, or stakeholder\-facing reporting packages.
- Ability to incorporate AI\-generated summaries, recommendations, contextual insights, and automated narratives into reporting environments.
### Data Integration \& Workflow Architecture
- Experience designing and optimizing ETL/ELT pipelines for reporting, automation, and AI workflow support.
- Experience integrating data from complex enterprise environments such as SAP, Microsoft 365, SharePoint, databases, APIs, cloud platforms, and flat files.
- Working knowledge of cloud data platforms such as Azure Data Factory, Snowflake, Azure Synapse Analytics, Databricks, or similar technologies.
- Strong understanding of data quality, data lineage, semantic modeling, scalable integration patterns, and automation reliability.
Preferred Experience
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- Implementation of enterprise Copilot solutions.
- Development of autonomous agents or multi\-agent systems.
- Microsoft 365 integration and automation, including SharePoint, Teams, Outlook, and Power Platform workflows.
- SAP integration for intelligent workflows and automated reporting processes.
- Business process automation, workflow orchestration, task routing, and approval automation.
- AI\-enabled knowledge management solutions and enterprise knowledge retrieval patterns.
- Exposure to traditional machine learning use cases such as anomaly detection, forecasting, classification, clustering, or regression is a plus, but not the primary focus of this role.
Core Competencies
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- Intelligent workflow design
- AI agent development
- Enterprise automation architecture
- Generative AI solution development
- Systems integration and data connectivity
- Business process transformation
- Technical problem solving
- AI governance and responsible deployment
- Technical mentorship
- Stakeholder collaboration
- Strategic automation planning
- Continuous improvement and innovation
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. 1Finity 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$121,100 to $193,760 USD. Additionally, this role may be eligible for a short\-term incentive based on company results and individual performance.As a technology company, 1Finity 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, 401K, and other benefits.\#Americas\_priorityRelocation Supported: No
Visa Sponsorship Approved: No
At 1Finity, one of our corporate principles is "We respect human rights”. This commitment guides the actions of every1Finity Group member and is fundamental to how we operate, both as individuals and as a company. We are proud to be an equal opportunity employer that values diversity and inclusion. Qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, 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, our customers, and our partners. California Privacy Act: https://www.fujitsu.com/us/Images/CALIFORNIA\-CONSUMER\-PRIVACY\-ACT\-NOTICE.pdf
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 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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