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The Work
The M365 Automation and AI Developer will manage mission\-critical collaboration and content management systems, integrating AI capabilities such as intelligent search, workflow automation, and context\-aware assistance. The role requires strict adherence to security, privacy, and compliance standards, including the implementation of robust access controls, data protection mechanisms, and auditing practices in accordance with stringent government and departmental policies.
This position will collaborate closely with stakeholders, system owners, security teams, and end users to translate business requirements into secure, scalable, and supportable M365 and SharePoint AI solutions that drive operational efficiency and mission success. Key Responsibilities
- Stay current with Department AI initiatives and systems, including StateChat (CoDrafter, customGPT), Northstar, Copilot, ORION, AIRE, and other emerging technologies, and evaluate their applicability to SharePoint and M365 solutions.
- Collaborate with stakeholders across the bureau and department to identify opportunities for process innovation, automation, and technology enhancement that improve mission effectiveness.
- Maintain awareness of evolving Microsoft 365 and SharePoint capabilities, patterns, and best practices, and recommend enhancements to existing solutions and environments.
- Ensure all design, development, configuration, and operations activities align with Department security, privacy, and responsible AI practices, including adherence to applicable policies, standards, and governance frameworks.
- Manage the overall health, configuration, security, performance, and modernization of SharePoint farms, site collections, sites, and service applications in support of mission\-critical collaboration and content management.
- Serve as the Tier 3 technical lead for complex troubleshooting, migration, and development efforts, including escalation support for lower\-tier teams.
- Design, develop, test, implement, and deploy SharePoint infrastructure and solution components (e.g., site architectures, content types, workflows, automation, integrations) that are secure, scalable, and maintainable.
- Perform administrative and office management tasks, including documentation, status reporting, and coordination activities, to support efficient information flow and operational effectiveness within the office.
- Develop, implement, and enforce SharePoint governance plans, including site provisioning processes, information architecture standards, and data retention and disposition policies.
- Drive innovation within the M365 and SharePoint environments by identifying, prototyping, and implementing new features, Power Apps, and Power Automate workflows to streamline business processes and improve user experience.
- Customize SharePoint sites through the design and configuration of templates, branding, navigation, and web parts to align with organizational needs, usability standards, and accessibility requirements.
- Research, design, and deploy SharePoint agents—customizable virtual assistants grounded in specific site content—to answer user queries, summarize documents, and support self\-service information discovery.
- Define the scope and purpose of each agent, including target user groups, priority use cases, and success metrics that align with mission and Department AI objectives.
- Conduct stakeholder consultations and workshops to identify requirements, refine use cases, and prioritize features for each agent implementation.
- Identify, assess, and organize content sources within SharePoint sites, document libraries, and lists that will ground the agent’s responses, ensuring completeness, accuracy, and appropriate classification.
- Configure agent settings—including tone, response style, conversation parameters, and escalation paths—to align with organizational culture, policies, and user expectations.
- Train and test agents using representative sample queries to validate accuracy, relevance, clarity, and appropriate handling of sensitive or out\-of\-scope requests.
- Plan and conduct user acceptance testing (UAT) with representative stakeholders, capturing feedback and issues for resolution.
- Document test cases, test results, defects, and resolutions, and implement necessary refinements to improve agent performance and reliability.
- Establish governance protocols for SharePoint agents, including content ownership, update and re\-training procedures, and quality standards for responses.
- Integrate authentication and access controls so that agents respect existing SharePoint permissions and do not expose unauthorized content.
- Regularly audit and manage permissions, data sources, and agent configurations to ensure agents only access authorized information and adhere to responsible AI and data protection practices.
- Create clear, role\-appropriate user documentation and training materials (e.g., user guides, FAQs, quick reference cards) to facilitate adoption and proper use of SharePoint agents.
- Develop troubleshooting resources and self\-help materials to reduce support burden and encourage self\-service usage.
- Conduct training sessions, demos, and briefings for end users, stakeholders, and support staff to promote awareness, understanding, and effective utilization of agents.
- Implement feedback mechanisms (e.g., in\-agent feedback prompts, surveys, support channels) to capture user input and continuously refine agent behavior and knowledge sources.
- Monitor usage analytics and metrics to track engagement, identify common queries, detect gaps in coverage, and measure agent effectiveness against established success criteria.
- Generate regular reports on agent performance, adoption, and impact, and present recommendations for improvements, expansion, or retirement as appropriate.
- Develop and execute maintenance and update plans to ensure agents remain current as content, policies, and user needs evolve.
- Demonstrate extensive experience with SharePoint list and library configuration, including field types, custom forms, views, content types, and comprehensive use of Site Settings options to manage features, permissions, and site configuration.
- Implement records management policies and out\-of\-the\-box solutions, including retention labels, document lifecycle management, and disposition processes in accordance with organizational requirements.
- Apply practical knowledge of out\-of\-the\-box workflows and no\-code/low\-code automation solutions, including creating reusable, site\-level, and list\-level workflows using tools such as Power Automate and SharePoint Designer (where applicable).
- Deliver no\-code design solutions by leveraging Publishing and Wiki pages, Master Pages (in classic environments), CSS, Themes, and Page Layouts to create intuitive, branded, and accessible user interfaces.
- Utilize and configure out\-of\-the\-box web parts, including connections, filters, and modern web part capabilities, to build dynamic, user\-centric pages and dashboards that support business and mission requirements.
- Other duties as assigned.
Qualifications – Here’s What You Need
- Minimum of 8\+ years of progressive IT experience, including at least 4 years of specialized experience in designing, developing, and administering SharePoint platforms in enterprise or government environments.
- Demonstrated experience planning and executing migrations between multiple SharePoint versions (e.g., SharePoint 2007 through SharePoint 2016\), including content, customizations, and workflows.
- Experience working with cloud\-based applications and collaboration platforms, such as Microsoft 365/Office 365 and Google for Government, in support of large, distributed user communities.
- Deep, hands\-on knowledge of SharePoint architecture, configuration, customization, and administration, including both classic and modern experiences.
- Proficiency with PowerShell for administration, automation, and scripting; working knowledge of SQL Server and Active Directory as they relate to SharePoint and M365 integration, authentication, and security.
- Practical experience with the Microsoft Power Platform, including Power Apps and Power Automate, to design and implement low\-code business applications and workflows.
- Experience using SharePoint Designer and other out\-of\-the\-box configuration tools to create workflows, customize forms, and configure site functionality.
- Proficiency with Microsoft Visio for creating business process and technical diagrams to support solution design, documentation, and stakeholder communication.
- Working knowledge of InfoPath for legacy form solutions and the ability to maintain, troubleshoot, or migrate InfoPath\-based forms where required.
- Microsoft certification for SharePoint 2010 or higher (e.g., MOS or equivalent Microsoft SharePoint certification) strongly preferred.
- Demonstrated experience supporting solutions within a U.S. federal agency environment; experience with the Department of State is preferred.
- Experience managing and supporting solutions in classified (SIPR) and/or unclassified (NIPR) environments, including adherence to applicable security, compliance, and requirements.
- Secret Security Clearance is required.
- Must be able to pass a background check. May require additional background checks as required by projects and/or clients at any time during employment.
Minimum Skills:* Exceptional interpersonal skills with the ability to communicate in a clear, professional, and articulate manner.
- Exceptional verbal and written communication skills.
- Excellent organizational, analytical, and problem\-solving skills with high\-level attention to detail.
- Proven ability to multitask and prioritize in a fast past environment with changing priorities; adaptable to change and a quick learner.
- Must be self\-motivated and able to work well independently as well as on a multi\-functional team.
- Ability to handle sensitive and confidential information appropriately
- Proficient in MS Office, Word, Outlook, PowerPoint, and Excel.
Our Commitment to you / overview of benefits* Medical, Dental and Vision Insurance; Wellness Program
- Flexible Spending Accounts (Healthcare, Dependent Care, Commuter)
- Short\-Term and Long\-Term Disability options
- Basic Life and AD\&D Insurance (Company Provided)
- Voluntary Life and AD\&D options
- 401(k) Retirement Savings Plan with matching after one year
- Paid Time Off
Reports to: Program Manager Working Conditions* Professional office environment, with the ability to work onsite in the main office.
- Must be physically and mentally able to perform duties extended periods of time.
- Ability to use a computer and other office productivity tools with sufficient speed to meet the demands of this position.
- Must be able to establish a productive and professional workspace.
- Must be able to sit for long periods of time looking at computer screen.
- May be asked to work a flexible schedule which may include holidays.
- May be asked to travel for business or professional development purposes.
- May be asked to work hours outside of normal business hours.
Other Duties: *Please note this job description is not designed to cover or contain a comprehensive list of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice.*
*Cayuse is an Equal Opportunity Employer. All employment decisions are based on merit, qualifications, skills, and abilities. All qualified applicants will receive consideration for employment in accordance with any applicable federal, state, or local law.*
Salary Context
This $150K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Cayuse Holdings, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($160K) sits 12% below the category median. Disclosed range: $150K to $170K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Cayuse Holdings AI Hiring
Cayuse Holdings has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Washington, DC, US, Austin, TX, US. Compensation range: $166K - $170K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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