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About This Role
Aurrera Health Group is seeking a part\-time AI Applications Consultant to support the design, development, testing, documentation, and refinement of AI\-enabled tools and workflows. This is a project\-based contractor engagement intended to add specialized technical execution capacity as Aurrera Health explores practical, responsible, and secure uses of AI to improve internal operations and client\-facing work. The ideal consultant is resourceful, practical, and comfortable figuring out how to move work forward when the path is not fully defined. We are looking for someone who can ask good questions, test possible approaches, learn quickly, document decisions, and deliver usable tools without requiring extensive oversight.
The consultant will coordinate with Aurrera’s AI strategic enablement lead and other internal stakeholders to help convert identified opportunities into working prototypes, documented workflows, and usable tools. The ideal consultant is highly self\-directed, comfortable working through ambiguity, and able to build practical solutions with limited oversight.
This engagement is intended to provide specialized AI development and workflow automation support that is not currently available within existing internal staff capacity. Aurrera Health will use this initial engagement to assess the effectiveness, scalability, and long\-term operational value of these tools before determining whether additional investment or internal resourcing is warranted.
Who We Are
Aurrera Health Group is a mission\-driven national health policy, strategy, and operations consulting firm with deep expertise designing and administering public programs, including Medicaid, Medicare, behavioral health, trauma\-informed care, and other safety net programs. Much of our firm’s work is in California, but our clients span state and federal Medicaid agencies, county/local health services departments, health care foundations, consumer advocacy organizations, hospitals, health systems, and trade associations.
Aurrera Health Group is a woman\-owned firm guided by our commitment to our mission – advancing access to affordable, comprehensive, equitable, high\-quality health coverage and care – and our values. We select our clients, projects, and staff in keeping with that mission. We pride ourselves on our relationship\-driven approach to customer service, our ability to adapt to changing landscapes, and our innovative spirit. We value and reward the quality of work we do for clients and support a range of efforts to ensure that employees can enjoy a healthy work\-life balance. We are committed to the growth and professional development of all our staff.
Scope of Work
The consultant may support work such as:
- Building and iterating on AI\-enabled tools, agents, and automated workflows based on defined project priorities.
- Supporting ongoing data integration and data sharing across tools, including API\-based workflows and integrations with platforms such as NetSuite, Paylocity, Microsoft 365 tools, or other systems as appropriate.
- Helping reduce manual data upload processes through automation where feasible and secure.
- Developing, testing, and documenting workflows so tools can be maintained, reused, or handed off.
- Evaluating AI outputs for accuracy, usability, quality, and appropriate use.
- Supporting internal data hygiene, metadata organization, and structured approaches to using operational data.
- Producing clear documentation, playbooks, and deployment or training materials for non\-technical users.
- Advising on practical implementation considerations, including tool selection, access controls, security, and maintainability.
- Coordinating with Aurrera stakeholders to clarify requirements, confirm priorities, and complete agreed\-upon deliverables.
Initial Six\-Month Deliverable Framework
Final deliverables will be defined in the Statement of Work, but the initial engagement is expected to include the following:
Phase 1: Orientation, Inventory, and Priority Build Planning
Estimated Months 1–2
- Review existing AI\-enabled tools, workflows, documentation, and known needs.
- Produce a written project inventory and prioritized backlog.
- Recommend immediate improvements to existing tools or workflows.
- Confirm documentation standards and project communication expectations.
- Support the development of an internal intake process for support/build requests.
Phase 2: Priority Enhancements and Workflow Development
Estimated Months 2–4
- Complete 2–3 defined enhancements, prototypes, or workflow implementations.
- Support API, automation, or data\-sharing improvements where feasible.
- Document build decisions, maintenance considerations, and known limitations.
- Identify security, governance, or tool approval issues that require internal review.
- Support staff training and enablement efforts including reviews of staff training materials and setting up internal champions to start generating, editing, and deploying their own applications.
Phase 3: Expanded Builds, Documentation, and Handoff
Estimated Months 4–6
- Complete 2–3 additional builds, refinements, or workflow implementations based on the approved backlog.
- Update playbooks, deployment materials, and user\-facing documentation.
- Support at least one tool or workflow moving into active internal use, if feasible.
- Provide a written assessment of what is working, remaining gaps, recommended next steps, and options for renewal or expanded scope.
- Continued support of staff training and enablement efforts.
Required Qualifications
Successful candidates should demonstrate:
- At least two years of relevant professional experience.
- Demonstrated experience building AI\-enabled tools or workflows in a professional, client\-facing, operational, or substantive independent context.
- Experience with at least one project that required ambiguity management, iteration, and delivery to a real user or use case.
- Experience with LLM APIs, AI workflow tools, automation tools, or agentic workflows.
- Ability to design and iterate on AI\-enabled workflows, including system behavior, context management, output testing, and quality evaluation.
- Experience with at least one agentic or retrieval\-augmented generation, or RAG\-based, workflow in a production or near\-production setting.
- Strong written and verbal communication skills, including the ability to create clear documentation without heavy prompting.
- Ability to work independently, organize work against agreed\-upon deliverables, and communicate proactively about progress, risks, and blockers.
- Demonstrated resourcefulness in solving ambiguous technical or workflow problems, including the ability to research options, test approaches, troubleshoot barriers, and move from concept to usable deliverable.
- Comfortable working remotely and asynchronously in a deliverables\-based contractor model.
- Working knowledge of data security best practices, including data handling, access controls, and responsible use of AI tools in professional environments with confidential information.
- Ability to explain technical concepts clearly to non\-technical stakeholders.
Preferred Qualifications
The following are helpful but not required:
- Familiarity with the Microsoft 365 ecosystem, including Copilot Studio, Power Automate, SharePoint, Teams, and related tools.
- Experience with workflow automation, internal operations tools, lightweight application development, or dashboard development.
- Experience with APIs and integrations involving ERP, HRIS, CRM, financial, or project management systems.
- Familiarity with NetSuite, Paylocity, or similar enterprise platforms, particularly for reporting or data integration use cases.
- Proficiency with Git\-based version control workflows, including branching, pull requests, and code review.
- Familiarity with Agile or sprint\-based delivery methods.
- Strong relational database skills, including schema design, SQL queries, structured data, and reporting outputs.
- Experience with scripting languages such as Python, JavaScript, or similar tools.
- Experience with CI/CD pipelines such as Azure Pipelines, GitHub Actions, or similar.
- Familiarity with hybrid data architecture, including when to use relational databases versus document or NoSQL stores.
- Experience building analytics layers on top of operational data, including defining metrics and structuring data for reporting.
- Experience supporting non\-technical users in adopting new tools or workflows.
- Familiarity with data visualization principles or tools such as Power BI, Tableau, or equivalent.
- Experience in health policy, consulting, research, nonprofit, or mission\-driven professional services environments.
What Success Looks Like
A successful engagement will result in:
- Aurrera has a clearer inventory and backlog of AI\-enabled tool and workflow opportunities.
- Existing tools are improved, documented, and easier to maintain.
- At least one tool or workflow is in active use or ready for broader internal testing.
- Company spends less time on technical execution and maintenance and more time on strategy, client needs, and prioritization.
- Technical decisions are documented clearly enough for internal stakeholders to understand the purpose, risks, and maintenance needs.
- The consultant demonstrates a resourceful, iterative approach to problem\-solving: asking clarifying questions, identifying workable options, testing and refining solutions, and escalating risks or constraints appropriately.
- Non\-technical users can understand how to use the tools or workflows developed.
- Security, data privacy, access, and responsible AI considerations are identified and escalated appropriately.
Engagement Details
This is a part\-time, project\-based, hourly independent contractor engagement. The initial engagement is expected to run for six months, with a target start date of July 1, 2026 and potential renewal based on project needs, available budget, and performance against agreed\-upon deliverables.
Estimated level of effort is equivalent to approximately 15–20 hours per week, depending on project phase and deliverable needs. Compensation range is $55\-$75 per hour DOE.
Application Materials
Candidates should submit:
- Resume or consultant profile.
- Brief cover letter or statement of interest describing relevant AI, automation, workflow, or application development experience.
- Links to, or brief descriptions of, 1–3 relevant project examples. These may include AI\-enabled tools, workflow automations, prototypes, technical documentation, dashboards, integrations, or other relevant applied technology projects.
- Hourly rate expectations.
- General availability and anticipated weekly capacity.
Candidates should not submit confidential, proprietary, or client\-sensitive materials. Project examples may be summarized or redacted as needed.
Salary Context
This $114K-$156K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Aurrera Health Group, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($135K) sits 24% below the category median. Disclosed range: $114K to $156K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Aurrera Health Group AI Hiring
Aurrera Health Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $156K - $156K.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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