Interested in this AI/ML Engineer role at Baptist Health South Florida?
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About This Role
Baptist Health is the region's largest not\-for\-profit healthcare organization, with 12 hospitals, over 29,000 employees, 4,500 physicians and 200 outpatient centers, urgent care facilities and physician practices across Miami\-Dade, Monroe, Broward and Palm Beach counties. With internationally renowned centers of excellence in cancer, cardiovascular care, orthopedics and sports medicine, and neurosciences, Baptist Health is supported by philanthropy and driven by its faith\-based mission of medical excellence. For 26 years, we've been named one of Fortune's 100 Best Companies to Work For, and in the 2025\-2026 U.S. News \& World Report Best Hospital Rankings, Baptist Health was the most awarded healthcare system in South Florida, earning 63 high\-performing honors.
What truly sets us apart is our people. At Baptist Health, we create personal connections with our colleagues that go beyond the workplace, and we form meaningful relationships with patients and their families that extend beyond delivering care. Many of us have walked in our patients' shoes ourselves and that shared experience fuels out commitment to compassion and quality. Our culture is rooted in purpose, and every team member plays a part in making a positive impact – because when it comes to caring for people, we're all in.
At Baptist Health, we’re committed to supporting our employees at every stage of their journey, both personally and professionally. Our approach is rooted in a “grow our own” philosophy, designed to help our team members build meaningful, long\-term careers with us, supported by benefits that make a real difference, including:
- Career growth and development opportunities, with clear pathways and ongoing support
- Comprehensive health and wellness resources that go beyond traditional benefits
- A wellness program that can help employees eliminate their medical plan deductible, reducing out\-of\-pocket healthcare costs
- Tuition reimbursement to support continued learning and advancement
- And so much more
Together, these benefits and others reflect our commitment to caring for our people, so they can build fulfilling careers with us while making a meaningful impact every day.
Description:
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The primary purpose of this role is to lead the development and communication of target\-state architectures, roadmaps, and solutions for technologies at the product portfolio level. With time and experience, the individual in this role may support several technologies spanning multiple product portfolios and/or multiple complex architecture domains (e.g., business, application, data, infrastructure).This includes building high\- quality architecture solutions as well as providing guidance on the design and establishment of architecture standards that align with current and future business needs. This role assists in the development, and maintains the architecture for assigned area and drives the adoption of new technologies. To be successful, the individual in this role must have solution architecture experience as well as an understanding of technical trends and innovation. This individual must also have a strategic thought process, business acumen, and the ability to influence leadership. The individual in this role works with little oversight and provides coaching and direction to more junior level architects on the team.
The AI Architect owns overall architecture and technical governance for the enterprise AI platform and AI\-enabled solutions across Baptist Health. This role is responsible for ensuring that AI capabilities are designed, governed, and operationalize in a way that is coherent, scalable and aligned with enterprise standards. Individual in this role will work in close partnership with product, AI, and cloud engineering teams to ensure enterprise AI initiatives move from architectural intent to delivered business outcomes with speed, consistency and operational confidence. Lead architect will be involved in making architecture decisions spanning, AI platform, AI services and its integration, connectivity across data and semantic layer, security and governance guardrails required to operate AI safely and responsibly in a regulated healthcare environment. The architect leads architecture conversations with business, technical and executive stakeholders, sets technical direction for engineering teams building on the platform, contributes hands\-on to critical design and proof\-of\-concept work, and maintains Architecture Decision Records (ADRs) of Enterprise AI.
Estimated salary range for this position is $137846\.35 \- $179200\.26 / year depending on experience.
Qualifications:
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Degrees:
- Bachelors.
Additional Qualifications:
- Bachelor's degree in Computer Science,IT, or related field.
- 10\+ years' experience in technology disciplines.
- Experience with architecting highly scalable \& highly available systems,experience with mobile \& other emerging technology trends.
- Ability to synthesize conclusions from research,compare various solutions \& plot complex technology roadmaps.
- Broad experience across business,application, information,infrastructure \& security architecture domains.
- Broad experience across business analysis,product design,engineering,QA, operations \& sustainment in a large corporation/enterprise environment.
- Technical experience in consumer/employee facing enterprise systems.
- Expertise in technical architecture,application systems design \& integration in a large heterogeneous enterprise environment with hands on experience in MicroServices, Java/J2EE, XML, Websphere/Weblogic/Apache TomCat Application Server,Rules Engine, Oracle or DB2 programming methodologies.
- Expert understanding of the code/coding practices.
- Demonstrable analytical \& problem solving skills.
- Deep understanding/experience of AWS,Azure cloud computing environments \& services.
- Excellent communication \& relationship skills, especially the ability to articulate advanced technical topics to both technical/non\-technical staff.
- Strong presentation skills.
Key Responsibilities
- Own end\-to\-end architecture for the enterprise AI platform across its core domains: model gateway and routing, agent orchestration and registry, context and memory services (RAG, semantic/vector, knowledge graph), data services, observability, and security/governance.
- Define the multi\-model strategy and routing architecture across Google Gemini Enterprise and Anthropic Claude (via Vertex AI, Bedrock, and/or direct API), including model selection criteria, cost/performance tradeoffs, failover, and vendor\-neutral abstraction patterns.
- Architect the agentic AI foundation: agent registry and system\-of\-record design, agent\-to\-agent (A2A) interoperability, Model Context Protocol (MCP) gateway placement and enforcement, tool\-calling contracts, autonomy tiering, and human\-in\-the\-loop approval patterns for actions with clinical, financial, or PHI impact.
- Define architecture for agent and non\-human identity and access management — verifiable, least\-privilege, fully auditable identities and credentials for AI agents, MCP servers, and automated workloads.
- Design the enterprise context and memory architecture: RAG pipelines, semantic/vector search, and knowledge graph patterns that support both conversational agents and downstream automation, with clear data governance boundaries between clinical, operational, and business data.
- Define architecture patterns for integrating AI solutions with clinical systems, with particular attention to Epic EHR touchpoints, ensuring PHI handling, minimum\-necessary access, and clinical safety review gates are embedded in the design rather than bolted on.
- Lead design of the AI security and governance layer: data loss prevention for PHI/PII in prompts and outputs, model access controls (RBAC/least\-privilege), audit logging and traceability, and alignment with HIPAA, HITECH, and applicable state and federal regulatory requirements.
- Architect defenses against AI\-specific threats: prompt injection, jailbreaks, indirect prompt injection through retrieved content or tool outputs, model abuse, data exfiltration via agent tool use, unsafe MCP server behavior, agent manipulation, and shadow\-AI risk across the enterprise.
- Establish guardrails against hallucination and unauthorized tool invocation, including content and safety filters, output validation, and hard limits on agent autonomy for high\-impact actions.
- Establish AI\-platform observability and LLMOps standards: usage and cost telemetry, latency and quality monitoring, model evaluation frameworks, drift detection, regression suites, and incident\-response playbooks for AI\-specific failure modes.
- Serve as the primary technical escalation point for AI architecture across the enterprise; participate in architecture review boards, AI governance committees, and executive steering forums; provide formal sign\-off against Definition of Done for each platform milestone.
- Author and maintain Architecture Decision Records (ADRs), reference architectures, to keep architecture decisions transparent, consistent and defensible.
- Mentor and provide technical direction to AI, platform, data, and security engineers delivering against the roadmap; enforce architectural consistency across all AI workstreams and service domains.
- Partner with Legal, Compliance, Privacy, and Clinical Informatics to ensure AI solution designs satisfy regulatory, ethical, and clinical safety requirements before production release.
Required Qualifications
- 10\+ years of professional solutions architecture experience, with at least 5 years in a lead architect role designing and delivering cloud or AI/ML solutions at enterprise scale.
- Hands\-on experience architecting solutions on Google Cloud Platform (GCP), including IAM, networking, GKE, and Vertex AI services, working knowledge of AWS cloud (IAM, EKS/ECS, Bedrock).
- Direct experience architecting or integrating enterprise deployments of Google Gemini Enterprise and Anthropic Claude (via API, Vertex AI, or Bedrock), including model routing, prompt and context management, and cost governance across multiple model providers.
- Proven experience designing agentic AI systems: multi\-agent orchestration, tool / function\-calling contracts, agent registries, and interoperability standards (A2A, MCP, or comparable protocols).
- Hands\-on experience with an agent / MCP gateway (e.g., Apigee X or equivalent) as the governed control point for agent and tool traffic — routing, authN/authZ, registration and discovery, rate limiting, policy enforcement, and observability.
- Strong understanding of AI / LLM security and governance: PHI and PII handling in prompts and completions, prompt\-injection and jailbreak defenses, indirect injection through retrieved content, model access controls, guardrails, and audit / traceability in a regulated industry.
- Experience with agent and non\-human identity and access management — least\-privilege, verifiable, and auditable identities and credentials for AI agents and automated workloads.
- Experience with Infrastructure as Code (Terraform) and CI/CD pipeline architecture (GitHub Actions, GitLab CI, or equivalent) for deploying and governing AI services at scale.
- Working knowledge of vector databases, semantic search, and knowledge graph technologies (PostgreSQL extensions, dedicated vector stores, or graph databases) supporting retrieval\-augmented generation.
- Understanding of distributed\-systems principles — eventual consistency, failure handling, idempotency, resilience patterns — as applied to AI and agentic workloads.
- Experience operating in a healthcare or other highly regulated environment, with familiarity with HIPAA and clinical data\-handling requirements; direct experience with Epic or another major EHR is a strong plus.
- Demonstrated experience producing Architecture Decision Records, reference architectures, and comparison / TCO analyses to drive structured, defensible technology decisions.
- Excellent written and verbal communication, with the ability to present architecture decisions to executive, clinical, and technical stakeholders.
- Bachelor’s degree in computer science, Engineering, or a related field, or equivalent practical experience.
Minimum Required Experience: 10 Years
EOE, including disability/vets
Salary Context
This $137K-$179K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Baptist Health South Florida, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($158K) sits 28% below the category median. Disclosed range: $137K to $179K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Baptist Health South Florida AI Hiring
Baptist Health South Florida has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in FL, US. Compensation range: $179K - $179K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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