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About This Role
About the Role:
Netsmart is seeking a Lead Software Architect, Cloud \& AI Modernization to support our Post\-Acute engineering organization. This role will help guide architecture across complex healthcare technology solutions that support providers, clinicians, and communities across post\-acute care.
We are looking for a software architecture leader who can work close to the development teams, understand existing application architecture, and help modernize products as Netsmart continues to scale cloud\-based and AI\-enabled capabilities. This person should bring a strong software development foundation, practical AWS/cloud knowledge, DevOps partnership experience, and the ability to operate effectively across a matrixed organization.
This role is onsite in Overland Park, KS, and candidates must live in or relocate to the Greater Kansas City metro area.
What You’ll Do:
- Lead architecture strategy and technical design for Post\-Acute applications and platforms.
- Work hands\-on with engineering teams to evaluate current\-state architecture, identify modernization opportunities, and guide practical execution.
- Support legacy application modernization through modular architecture, API design, service decomposition, cloud migration patterns, and scalable engineering practices.
- Partner with DevOps, Hosting, Cloud, Security, Product, UX, and engineering leaders to align architecture decisions with delivery needs and operational realities.
- Provide technical leadership through design reviews, code reviews, architecture documentation, proof\-of\-concepts, and reusable patterns.
- Guide engineering teams on AWS architecture, distributed systems, CI/CD, observability, reliability, security, and performance.
- Identify opportunities to responsibly integrate AI, automation, analytics, or data\-driven features into Post\-Acute products and workflows.
- Translate complex technical tradeoffs into clear recommendations for business, product, and technology leaders.
- Help teams balance modernization goals with client commitments, regulatory expectations, quality, and production stability.
- Promote engineering standards that improve maintainability, scalability, reliability, and speed of delivery
What You’ll Bring
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- Strong software engineering background with experience as a Software Architect, Lead Engineer, Technical Lead, Senior Architect, or similar role.
- Hands\-on experience with enterprise\-scale application development and modernization.
- Experience working with legacy applications and helping teams evolve them toward more scalable, cloud\-enabled architecture.
- Strong understanding of AWS, cloud\-native patterns, distributed systems, APIs, microservices or modular architecture, CI/CD, observability, and DevOps practices.
- Experience in one or more relevant development ecosystems such as Java, .NET, C\#, SQL Server, PostgreSQL, or similar enterprise technologies.
- Ability to earn credibility with engineers by connecting architecture direction to real development and operational realities.
- Strong communication skills with the ability to influence without direct authority in a matrixed environment.
- Interest in AI, machine learning, automation, or data\-driven product capabilities, with a practical view of how to bring those capabilities into production responsibly.
- Bachelor’s degree or equivalent relevant experience.
- Must be onsite in Overland Park, KS.
Preferred Experience
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- Healthcare technology, EHR, clinical workflow, human services, post\-acute, care management, or regulated industry experience.
- Experience modernizing complex enterprise applications in AWS.
- Experience with Kubernetes, infrastructure\-as\-code, event\-driven systems, DevOps, or platform engineering.
- Exposure to AI/ML, LLMs, MLOps, responsible AI, or intelligent automation within enterprise software.
- Experience partnering with Product and Engineering to translate client or market needs into technical architecture.
What Success Looks Like
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Success in this role means you help Post\-Acute engineering teams modernize thoughtfully while continuing to deliver reliable, meaningful healthcare technology. You bring clarity to complex architecture decisions, partner well across the organization, and help teams strengthen cloud readiness, development practices, DevOps alignment, and future AI\-enabled product capabilities.
Why Netsmart
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At Netsmart, you will help build technology that truly matters. This role offers the opportunity to shape modern, cloud\-enabled and AI\-aware healthcare technology in a mission\-driven organization where innovation supports providers, clinicians, clients, and communities every day.
Netsmart is proud to be an equal opportunity workplace and is an affirmative action employer, providing equal employment and advancement opportunities to all individuals. We celebrate diversity and are committed to creating an inclusive environment for all associates. All employment decisions at Netsmart, including but not limited to recruiting, hiring, promotion and transfer, are based on performance, qualifications, abilities, education and experience. Netsmart does not discriminate in employment opportunities or practices based on race, color, religion, sex (including pregnancy), sexual orientation, gender identity or expression, national origin, age, physical or mental disability, past or present military service, or any other status protected by the laws or regulations in the locations where we operate.
Netsmart desires to provide a healthy and safe workplace and, as a government contractor, Netsmart is committed to maintaining a drug\-free workplace in accordance with applicable federal law. Pursuant to Netsmart policy, all post\-offer candidates are required to successfully complete a pre\-employment background check, including a drug screen, which is provided at Netsmart’s sole expense. In the event a candidate tests positive for a controlled substance, Netsmart will rescind the offer of employment unless the individual can provide proof of valid prescription to Netsmart’s third party screening provider.
*If you are located in a state which grants you the right to receive information on salary range, pay scale, description of benefits or other compensation for this position, please use this* *form* *to request details which you may be legally entitled.*
*All applicants for employment must be legally authorized to work in the United States. Netsmart does not provide work visa sponsorship for this position.*
*Netsmart's Job Applicant Privacy Notice may be found* *here**.*
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 Netsmart Technologies, 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. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Netsmart Technologies AI Hiring
Netsmart Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Overland Park, KS, US.
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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