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Ability to Obtain Public TrustAI Technical Scrum Master with Python Development
We are seeking an AI Technical Scrum Master with Python Development to lead Agile delivery for mission-focused, AI-enabled software products in a SAFe Agile environment while providing targeted, part-time Python development support.
This is an intentionally hybrid position, not two full-time roles combined into one. The primary responsibility is serving as the Scrum Master and Agile delivery lead for a cross-functional team. The secondary responsibility is contributing hands-on Python development for automation, APIs, integrations, data-processing utilities, testing, and selected technical backlog items based on sprint priorities and available capacity.
The successful candidate will facilitate Agile ceremonies, remove delivery impediments, strengthen team execution, coordinate dependencies, and foster continuous improvement across teams building trusted, secure, and scalable AI-enabled information systems. The candidate will also apply practical Python development skills to support delivery when additional technical capacity is needed, without compromising Scrum Master responsibilities, team coaching, or Agile leadership.
You will work closely with Product Owners, business stakeholders, Release Train Engineers, architects, software engineers, data scientists, testers, security teams, and operations teams to plan, coordinate, and deliver AI solutions that improve operational workflows, decision-making, and mission outcomes. The role requires strong Scrum Master leadership, experience with SAFe Agile delivery, an understanding of modern software engineering practices, and the ability to contribute maintainable Python code within an enterprise delivery environment.
This position is ideal for a technically capable servant leader who can enable a high-performing Agile team, communicate effectively with technical and non-technical stakeholders, and provide focused Python development support for well-defined backlog items.
Role Responsibility Model
Primary Role: Technical Scrum Master and Agile Delivery Lead
The primary focus of the position is Scrum Master leadership and Agile delivery. The individual is accountable for team facilitation, coaching, delivery coordination, impediment removal, continuous improvement, and support for successful sprint and Program Increment execution.
Secondary Role: Part-Time Python Developer
The secondary focus is hands-on Python development performed according to sprint priorities, team capacity, and technical need. Development assignments may include automation, APIs, integrations, data-processing utilities, testing tools, prototypes, and other clearly defined technical backlog items. The individual is not expected to perform the workload of a full-time Python developer in addition to full-time Scrum Master responsibilities.
What you will do
- Serve as the Technical Scrum Master and Agile Delivery Lead
- Serve as the Scrum Master for an Agile team delivering AI-enabled information systems in a SAFe Agile environment.
- Facilitate daily stand-ups, sprint planning, backlog refinement, sprint reviews, retrospectives, and other team synchronization activities.
- Support Program Increment Planning, iteration execution, dependency coordination, risk management, and alignment with broader program objectives.
- Partner with Product Owners, Release Train Engineers, technical leads, and stakeholders to promote transparency, delivery discipline, and predictable outcomes.
- Help maintain a healthy, prioritized, and sprint-ready backlog by facilitating effective collaboration among product, business, and technical stakeholders.
- Ensure Agile practices are applied consistently and pragmatically in alignment with SAFe principles and organizational delivery standards.
- Protect sufficient capacity for Scrum Master responsibilities while transparently planning any part-time Python development commitments.
- Enable Delivery of AI-Enabled Information Systems
- Support teams delivering AI-enabled capabilities such as document understanding, semantic search, summarization, recommendations, intelligent automation, conversational experiences, predictive analytics, and decision-support tools.
- Help the team manage AI-specific delivery considerations, including experimentation, iterative model improvement, data dependencies, integration readiness, model and prompt evaluation, and validation of AI-generated outputs.
- Coordinate across product, engineering, data science, testing, security, governance, and operations teams to help ensure AI capabilities are practical, usable, secure, supportable, and ready for release.
- Promote iterative delivery approaches that balance delivery speed, technical feasibility, quality, trust, governance, security, and mission value.
- Help translate complex technical work into clear plans, risks, dependencies, outcomes, and decisions for business and program stakeholders.
- Remove Impediments and Improve Team Performance
- Proactively identify, track, escalate, and help resolve impediments affecting delivery, quality, velocity, or team effectiveness.
- Coach team members on Agile practices, collaboration, self-organization, estimation, work management, accountability, and continuous improvement.
- Foster an environment of focus, transparency, inclusion, ownership, and continuous learning.
- Use Agile metrics and team insights to identify improvement opportunities and support more effective sprint and Program Increment execution.
- Help the team improve delivery flow, reduce bottlenecks, manage technical and delivery risks, and strengthen cross-functional coordination.
- Facilitate retrospectives that produce specific, achievable, and measurable improvement actions.
- Drive Cross-Team Coordination and Program Execution
- Coordinate dependencies, risks, constraints, decisions, and integration points across teams and stakeholders.
- Work with Product Owners and technical leads to clarify priorities, maintain sprint readiness, and establish achievable delivery commitments.
- Facilitate communication between technical and non-technical stakeholders to maintain alignment on scope, progress, risks, dependencies, and decisions.
- Maintain visibility into team progress, delivery health, impediments, technical challenges, and upcoming milestones.
- Contribute to release readiness and support the smooth transition of completed capabilities into testing, deployment, operations, and sustainment.
- Contribute Part-Time Python Development
- Contribute hands-on Python development for clearly defined technical backlog items after Scrum Master and Agile delivery responsibilities are appropriately planned and protected.
- Develop and maintain Python-based automation scripts, REST APIs, system integrations, data-processing utilities, testing tools, prototypes, and internal delivery utilities.
- Write clean, modular, reusable, documented, testable, and secure Python code in collaboration with software engineers, data scientists, and solution architects.
- Develop or support lightweight backend services and integrations using appropriate Python frameworks and libraries.
- Create unit and integration tests, troubleshoot defects, review logs, and support validation of Python components.
- Participate in Git-based source-control practices, pull requests, peer reviews, automated testing, CI/CD activities, defect resolution, and technical documentation.
- Work within established architecture, engineering, security, quality, and coding standards.
- Communicate development progress, risks, dependencies, and capacity constraints transparently during sprint execution.
- Avoid becoming the sole owner or single point of failure for business-critical Python components.
- Coordinate with the Product Owner and technical lead when Scrum Master duties or unplanned impediments affect development capacity or commitments.
- Support Quality, Governance, and Responsible Delivery
- Encourage Agile delivery practices that support quality, traceability, security, compliance, and operational readiness.
- Help incorporate architecture, governance, cybersecurity, testing, documentation, and release requirements into backlog planning and execution.
- Support delivery practices appropriate for systems processing PII, PHI, CUI, or other sensitive information.
- Promote responsible delivery of AI-enabled capabilities, including human-in-the-loop review, transparency, auditability, output validation, and appropriate governance controls.
- Ensure part-time development contributions follow the same engineering, testing, security, and documentation standards required of the broader development team.
- Champion Agile Maturity and Continuous Improvement
- Coach team members and stakeholders on SAFe Agile values, Scrum practices, and effective delivery behaviors.
- Help strengthen Agile maturity by sharing good practices, delivery insights, technical understanding, and lessons learned.
- Encourage collaboration, trust, adaptability, and evidence-based decision-making in response to evolving mission and business needs.
- Identify opportunities to use automation and responsible AI-assisted tools to reduce administrative effort and improve delivery visibility without replacing human judgment or accountability.
What you will need
- Must be able to OBTAIN and MAINTAIN a Federal or DoD "PUBLIC TRUST"; candidates must obtain approved adjudication of their PUBLIC TRUST prior to onboarding with Guidehouse. Candidates with an ACTIVE PUBLIC TRUST or SUITABILITY are preferred.
- Bachelor’s degree in Information Technology, Computer Science, Engineering, Business, Data Science, or a related field, OR an additional FOUR (4) years of relevant experience in lieu of a degree.
- Minimum FOUR (4) years of experience as a Scrum Master supporting enterprise software delivery teams.
- Demonstrated experience serving as a Scrum Master in a SAFe Agile environment, including sprint execution, Program Increment Planning, dependency coordination, risk and issue management, and cross-team collaboration.
- Strong knowledge of Agile, Scrum, Kanban, and SAFe delivery principles and practices.
- Proven ability to facilitate Agile ceremonies, coach teams, remove impediments, manage competing priorities, and drive continuous improvement.
- Experience supporting cross-functional teams that include Product Owners, engineers, architects, data scientists, testers, security personnel, and operations staff.
- Strong understanding of the software delivery lifecycle, including planning, design, development, testing, security, release, deployment, and operational readiness.
- Experience supporting delivery in enterprise environments with governance, compliance, security, quality, and documentation requirements.
- Experience supporting AI-enabled information systems, advanced analytics platforms, machine learning solutions, intelligent automation, or other data-driven applications.
- Minimum TWO (2) years of hands-on Python software development experience supporting production applications, enterprise solutions, automation, data engineering, or system integration use cases.
- Demonstrated proficiency with Python fundamentals, including object-oriented programming, data structures, modules, exception handling, logging, package management, and development of maintainable and reusable code.
- Experience developing or integrating REST APIs, backend services, automation scripts, system integrations, or data-processing workflows using Python.
- Experience with Python testing and quality practices, including unit testing, debugging, code reviews, static analysis, dependency management, and secure coding standards.
- Experience using Git-based source control, branches, pull requests, peer reviews, and modern CI/CD practices.
- Ability to translate technical backlog items into appropriately sized development tasks and communicate implementation risks, assumptions, dependencies, and tradeoffs.
- Demonstrated ability to balance primary Scrum Master responsibilities with part-time Python development based on sprint priorities, available capacity, and team needs.
- Ability to recognize and clearly communicate when Agile leadership obligations require reprioritization, reassignment, or adjustment of development commitments.
- Strong communication, facilitation, organization, problem-solving, and stakeholder-management skills.
What would be nice to have
- SAFe Scrum Master, Certified ScrumMaster, PMI Agile Certified Practitioner, or a similar Agile certification.
- Experience with Python frameworks and libraries such as FastAPI, Flask, Django, Pandas, NumPy, Pydantic, PyTest, or equivalent tools.
- Experience creating API specifications, data-validation models, automated tests, and reusable Python packages.
- Experience developing Python solutions that integrate with AWS services such as AWS Lambda, Amazon API Gateway, Amazon S3, Amazon Bedrock, Amazon SageMaker, AWS Glue, Amazon DynamoDB, or related cloud services.
- Familiarity with containerized Python applications, Docker, infrastructure automation, application monitoring, logging, and observability practices.
- Experience supporting Generative AI, large language model, or AI-assisted software products involving summarization, intelligent search, document understanding, conversational experiences, retrieval-augmented generation, or decision support.
- Familiarity with AI/ML delivery concepts such as model evaluation, prompt iteration, human-in-the-loop workflows, retrieval-augmented generation, confidence scoring, output validation, and governance controls.
- Experience working with regulated or sensitive data, including PII, PHI, CUI, retention requirements, auditability, and security controls.
- Experience with Jira, Confluence, Miro, or similar Agile planning and collaboration platforms.
- Experience in government, healthcare, human services, case management, grants management, or other mission-oriented operational environments.
- Experience partnering with Product Owners, Release Train Engineers, architects, technical leads, and leadership teams to improve Agile maturity, engineering discipline, and release outcomes.
- Experience using Generative AI-assisted tools to improve meeting preparation, sprint reporting, action tracking, risk identification, backlog refinement, software development, testing, and documentation while maintaining security, governance, and human oversight.
The annual salary range for this position is $98,000.00-$163,000.00. Compensation decisions depend on a wide range of factors, including but not limited to skill sets, experience and training, security clearances, licensure and certifications, and other business and organizational needs. What We Offer:
Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace.
Benefits include:
- Medical, Rx, Dental & Vision Insurance
- Personal and Family Sick Time & Company Paid Holidays
- Parental Leave
- 401(k) Retirement Plan
- Group Term Life and Travel Assistance
- Voluntary Life and AD&D Insurance
- Health Savings Account, Health Care & Dependent Care Flexible Spending Accounts
- Transit and Parking Commuter Benefits
- Short-Term & Long-Term Disability
- Tuition Reimbursement, Personal Development, Certifications & Learning Opportunities
- Employee Referral Program
- Corporate Sponsored Events & Community Outreach
- Care.com annual membership
- Employee Assistance Program
- Supplemental Benefits via Corestream (Critical Care, Hospital Indemnity, Accident Insurance, Legal Assistance and ID theft protection, etc.)
- Position may be eligible for a discretionary variable incentive bonus
About Guidehouse
Guidehouse is an Equal Opportunity Employer–Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation.
Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco.
If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1-571-633-1711 or via email at [email protected]. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation.
All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains including @guidehouse.com or [email protected]. Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process.
If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse’s Ethics Hotline. If you want to check the validity of correspondence you have received, please contact [email protected]. Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant’s dealings with unauthorized third parties.
Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.
Salary Context
This $98K-$163K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Guidehouse, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($130K) sits 38% below the category median. Disclosed range: $98K to $163K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Guidehouse AI Hiring
Guidehouse has 8 open AI roles right now. They're hiring across Data Engineer, Data Scientist, AI/ML Engineer. Positions span Washington, DC, US, Philadelphia, PA, US, Beavercreek, OH. Compensation range: $133K - $188K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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