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About This Role
Who We Are:
Managed Health Care Associates, Inc. (MHA) provides care communities access, solutions, and insights to help them run their businesses more effectively. Our members include post\-acute providers across the care continuum, including long\-term care, home infusion, specialty pharmacies, senior living, and other group living facilities. Our team of associates is passionate about our common mission of helping people age with grace and championing our core values of being Curious Learners, Selfless Advocates, and Relentless Finishers.
Who we’re looking for:
MHA is transforming its Technology division from a traditional, siloed waterfall model into an integrated, AI\-first, agentic software delivery organization built on the Microsoft Azure and M365 ecosystem. As the first dedicated AI engineering hire within the Solution Engineering department, the AI Solution Engineer reports to the Director of Solution Engineering and serves as the hands\-on technical driver who turns the Agentic SDLC vision into working practice across MHA's engineering squads.
This is a lead/solution engineer role—not a support function. The person in this seat drives MHA's agentic transformation, personally builds foundational tooling and guardrails, and serves as the organization's subject\-matter expert on agentic AI engineering, setting standards that squads adopt while those squads retain day\-to\-day delivery ownership. The role is equal parts builder and evangelist: standing up secure, well\-governed CI/CD pipelines; integrating agentic workflows into GitHub, Visual Studio Code, and GitHub Copilot as the engineers' day\-to\-day "agentic harness"; and continuously identifying high\-leverage ways to apply AI to accelerate MHA's products and internal delivery.
Success in this role advances MHA's core engineering principles—AI\-First Development, Shift\-Left Quality, You Build It You Own It, and Azure\-Native by Default—and supports enterprise targets such as the year\-end goal of 50% agentic code check\-in. Because MHA operates in a healthcare\-adjacent environment, this role also ensures agentic tools and workflows comply with MHA's responsible AI use policy and applicable data privacy requirements, including safeguarding PHI and other regulated data.
What You’ll Be Doing:
Agentic Engineering, Orchestration \& Context Design
- Establish and continuously refine the “agentic harness,” the integrated toolchain of GitHub, VS Code, GitHub Copilot, and Claude, that full\-stack Digital Solutions Engineers use for AI\-assisted, agentic development.
- Design and document agent orchestration patterns, including multi\-agent and subagent delegation, MCP (Model Context Protocol) tool integration, and context engineering practices, that let engineers reliably delegate implementation, testing, and review work to AI agents.
- Partner with squads to increase agentic code check\-in rates in support of MHA's year\-end adoption target, tracking and reporting progress through the METL framework.
- Serve as the internal subject\-matter expert on agentic coding practices, staying current on emerging AI development tools, MCP servers, and agent frameworks, and rapidly evaluating them for fit within MHA's Azure\-native environment.
- Design agentic workflows and tool integrations with interoperability in mind, ensuring agents, MCP servers, and enterprise systems (Jira, Azure DevOps, and platform APIs) orchestrate cleanly across squads rather than functioning as disconnected point solutions.
Evals, Quality \& Responsible AI Use
- Build and own MHA's evaluation framework for agentic and AI\-generated output, including automated evals, regression test suites, and human\-in\-the\-loop (HITL) checkpoints for higher\-risk agentic changes, closing what is currently one of the org's largest technical capability gaps.
- Define quality bars and pass/fail criteria for agentic contributions prior to merge, working alongside the SonarQube and Playwright gates already embedded in the pipeline.
- Ensure agentic workflows and AI tool usage comply with MHA's responsible AI use policy and applicable data privacy requirements, including safeguarding PHI and other regulated data from unintended exposure to AI tools or models.
- Partner with IT Operations \& Security on data\-handling guardrails (masking, redaction, access scoping) for any agentic workflow that could touch sensitive member, customer, or PHI\-adjacent data.
AI Security \& Governance
- Define and enforce AI\-specific security controls, including prompt\-injection defenses, agent permission scoping, sandboxing of agent execution environments, and tool/action allowlisting.
- Assess and mitigate secrets\-exposure and software supply chain risk introduced by AI\-generated code, third\-party MCP servers, and agent tool integrations.
- Partner with Security to fold these controls into the CI/CD pipeline and into the monthly Architecture Review Board (ARB) review process.
CI/CD Pipeline \& DevSecOps
- Build out and harden MHA's CI/CD pipelines across GitHub Actions and Azure DevOps/Azure, embedding automated security scanning (SAST/DAST, dependency and secret scanning) and quality gates directly into the pipeline.
- Integrate SonarQube, Playwright, and related tooling so that code quality, test coverage, and security posture are enforced automatically, shifting quality left rather than relying on a separate QA handoff.
- Define and monitor pipeline health metrics (build reliability, deployment frequency, defect escape rate, mean time to remediate) that feed the Operational Excellence and Delivery \& Enablement domains.
LLMOps, Observability \& Cost Governance
- Stand up telemetry and tracing for agentic workflows, including token spend, latency, and success/failure rates, to give engineering leadership visibility into agent and model performance.
- Implement token spend and cost monitoring alongside model routing/fallback strategies that balance output quality against cost.
- Feed observability data into scorecards and ARB reviews to support data\-driven decisions on tool and model selection.
Cross\-Functional Leadership \& Enablement
- Work directly with Digital Solutions Engineering, Data, Analytics \& Engineering, IT Operations \& Security, and Agile Delivery Management to embed agentic practices, standards, and process improvements across their teams, serving as SME and standard\-setter rather than as the delivery owner for their squads' work.
- Develop lightweight internal training, playbooks, and office hours, favoring low\-cost channels such as Microsoft Learn, to build engineering\-wide fluency in agentic tools.
- Identify and prototype novel, high\-leverage applications of AI across Claude, OpenAI, and Azure AI services to accelerate product development and business\-facing solutions, prioritizing by measurable impact rather than novelty.
- Act as a thought partner to the Director of Solution Engineering and the SVP Technology on the technical roadmap for the Agentic SDLC program and represent AI engineering considerations in team level planning and Program Increment (PI) execution.
What You’ll Bring to the Table:
- 5\+ years of professional software engineering experience, including hands\-on delivery in cloud\-native environments.
- Demonstrated depth in agentic AI development, including prompt engineering, agent orchestration, subagent design, MCP (Model Context Protocol) tool integration, and context engineering, not just familiarity with AI code\-completion tools.
- Experience building or applying evaluation frameworks for AI/agent output, such as automated evals, regression testing for agentic changes, or human\-in\-the\-loop (HITL) review processes.
- Working knowledge of AI\-specific security practices, including prompt\-injection mitigation, agent permission scoping, sandboxing, and tool/action allowlisting.
- Strong working knowledge of Microsoft Azure and the M365 ecosystem, including Azure DevOps or GitHub Actions for CI/CD.
- Proven experience building or hardening CI/CD pipelines with embedded security scanning and automated quality gates (e.g., SAST, OWASP\-aligned practices, SonarQube, Playwright or equivalent test automation).
- Solid full\-stack engineering foundation, comfortable across application code, infrastructure\-as\-code, and release automation.
- Strong software engineering fundamentals independent of AI tooling: clean and maintainable code, thoughtful API and system design, robust automated testing (unit, integration, and end\-to\-end), and disciplined version control and code review practices.
- Experience designing for interoperability and orchestration across distributed systems and APIs, ensuring agents, MCP tools, and enterprise platforms integrate cleanly rather than as isolated point solutions.
- Understanding of data privacy and responsible AI obligations in a regulated, healthcare\-adjacent environment (e.g., HIPAA/PHI handling), with the judgment to ensure agentic tools and workflows don't expose protected or sensitive data.
- Excellent communication skills, with the ability to translate emerging AI capabilities into practical, adoptable practices for engineers who may be new to agentic workflows.
Preferred Qualifications
- Experience with LLMOps or agent observability tooling, including tracing, token spend monitoring, and model routing/fallback, in a production setting.
- Experience within a healthcare, GPO, or regulated enterprise technology environment, including direct exposure to PHI\-handling or HIPAA compliance requirements.
- Familiarity with Jira and modern agile delivery tooling used to plan, track, and orchestrate engineering work across squads.
- Exposure to agile scaled\-delivery models (SAFe\-style Program Increments, team\-based operating models) and performance frameworks.
- Prior experience standing up an AI or platform engineering capability from the ground up, ideally as an early or founding technical hire.
- Working knowledge of observability and monitoring tooling (Azure Monitor, Application Insights) and delivery/issue\-tracking platforms (e.g., Jira).
What’s Good to Know:
- Remote or on\-site environment
- Percentage of travelling required/otherwise enter n/a
Why Join MHA:
MHA continues to lead by providing purpose\-driven and value\-based solutions, which preserve the dignity and grace people deserve, regardless of age. Let us be the best place you’ll ever work!
Our associates enjoy the following benefits, and you can, too!
Staying Healthy
- Comprehensive medical, dental, vision and prescription plans with FSA/HSA options individual and family options
- Teledoc access
- Fitness Reimbursement
- Commuter Benefit Plan
- Access to an Employee Assistance Program (EAP)
Enjoying Time\-Off
- Paid Time Off
- Day off for your birthday and a floating holiday
- Paid Parental Leave
Planning for the Future
- 401K with a match
- Employee Stock Purchase Plan
- Life Insurance, short\-term \& long\-term disability insurance
- Access to financial and legal advisors
- Perks and Benefits Discounts
Learning Continuously
- Tuition Reimbursement
- E\-learning programs
- Ongoing Team Trainings
Making an Impact
- Paid volunteer time\-off
- Donation matching
Physical Demands
The physical demands and work environmental characteristics described here represent those that an employee must meet to perform the essential functions of this job successfully. Reasonable accommodation may be provided to enable individuals with disabilities to perform essential functions.
- Physical Demands: While performing the duties of this job, the employee is occasionally required to move around the work area; sit; perform manual tasks; operate tools and other office equipment such as computers, computer peripherals, and telephones; extend arms; kneel; talk and hear. The employee must occasionally lift and move up to 15 pounds.
- Mental Demands: Employees must follow directions, collaborate well with others, and manage high workloads.
- Work environment: The noise level in the work environment is usually minimal.
The Company
Managed Health Care Associates, Inc. (MHA) is a leading health care services and technology company that offers a growing portfolio of services and solutions to support the diverse and complex needs of the post\-acute health care provider. MHA provides expertise in Group Purchasing, Managed Care and Payer Contracting, Reimbursement Management, Specialty Pharmacy Solutions, Pharmaceutical Data Analytics, Consultant Pharmacy Software and Legislative Advocacy. Through the delivery of innovative health care services and solutions, MHA helps members increase operational efficiency, maximize business growth, and provide optimum care for their patients.
Founded in 1989, MHA was purchased by Roper Technologies, Inc. (http://www.ropertech.com) in 2013\. Roper Technologies is a constituent of the S\&P 500, Fortune 1000\. The Company operates market\-leading businesses that design and develop vertical software and technology enabled products for a variety of niche markets. To learn more please visit www.mhainc.com (http://www.mhainc.com).
Compensation:
The salary range for this role is $140,000\.00 \- $170,000\.00\.
This is the lowest to highest salary range for which this role has been evaluated. An employee’s position within the salary range will be based on several factors which include, but not limited to education, qualifications, certifications, experience, skills, geographic location, and business or organizational needs. We offer a comprehensive package of benefits which includes medical, dental, vision healthcare and other insurance benefits (for employee and family), retirement benefits, including 401(k), paid company holidays and paid time off.
*Managed Health Care Associates, Inc. is an Equal Opportunity Employer and ensures its employment decisions comply with principles embodied in Title VII, the Age Discrimination in Employment Act, the Rehabilitation Act of 1973, the Vietnam Veterans Readjustment Assistance Act of 1974, Executive Order 11246, Revised Order Number 4, and applicable state regulations.*
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $140K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Methodist Homes (MHA), 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. This role's midpoint ($155K) sits 28% below the category median. Disclosed range: $140K to $170K.
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.
Methodist Homes (MHA) AI Hiring
Methodist Homes (MHA) has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Parsippany-Troy Hills, NJ, US. Compensation range: $170K - $170K.
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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