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About This Role
Description:
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Under the supervision of the IT Operations Manager, the AI Engineer is a hands\-on builder responsible for designing, deploying, and operating artificial intelligence solutions across LC Plus Technology's client portfolio. The role builds retrieval\-grounded agents in Microsoft Copilot Studio and Azure AI Foundry, designs the data ingestion and integration patterns that make those agents reliable, and partners with client compliance, project management, and data analytics functions to ensure every solution respects the regulated environments in which the clients of LC Plus Technology operate. The engineer joins a small, focused technical team and is the voice on AI build feasibility across client conversations. The role mentors peers on Microsoft AI build practices, operates LC Plus Technology's AI Solution Inventory, and stays current on Microsoft's AI platform evolution so it can be translated into client roadmap implications. Initial focus for this role is anchored on managed care client work, with the scope expanding across the client portfolio over time.
Essential Competencies:
- Own design, build, deployment, and operation of AI solutions from intake through production retirement.
- Maintain audit\-readiness, security posture, and data integrity in every implementation.
- Document architecture decisions, data lineage, evaluation results, and operational runbooks.
- Translate business problems into solution architectures grounded in data quality, retrieval design, and governance.
- Evaluate when to apply Copilot Studio versus Azure AI Foundry versus traditional automation, with rationale tied to need and risk.
- Surface data foundation gaps before agent build, naming dependencies and risks proactively.
- Deliver solutions with rigorous validation, monitoring, rollback paths, and source citation.
- Treat every production AI capability as accountable software, not an experiment.
- Maintain high standards for grounding quality, prompt control, and output reliability.
- Collaborate with client compliance, project management, data, and business unit stakeholders to align solutions with operational reality.
- Translate technical constraints into plain language for non\-technical stakeholders, including client executive leadership.
- Identify when to push back on a use case and when to find a path forward.
- Work effectively with LC Plus Technology peers, client partner functions, external consulting partners, and vendor representatives.
- Support onboarding of new use cases through client intake processes.
- Mentor LC Plus Technology peers and client counterparts on Microsoft AI build practices.
- Demonstrate commitment to the LC Plus Technology mission and to the client\-first ethic in technical work.
- Uphold the consistency and accountability standard expected of all solutions that serve clients and the populations they serve.
- Operate with full HIPAA and BAA discipline within regulated client environments.
Requirements:
- Bachelor's degree in Computer Science, Data Engineering, Information Systems, Health Informatics, or a related discipline, or equivalent professional experience.
- Five or more years of software engineering or data engineering experience, with at least three years delivering AI or machine learning solutions into production.
- Demonstrable hands\-on experience designing, building, and shipping AI agents in Microsoft Copilot Studio (shipped solutions, not workshop exercises).
- Hands\-on experience with Azure AI Foundry, including project structure, model deployment, evaluation, and monitoring.
- Hands\-on experience implementing retrieval\-augmented generation (RAG) patterns, including vector stores (Azure AI Search, Cosmos DB Vector, or comparable), embedding models, chunking strategies, grounding, and source citation.
- Strong data engineering foundation: experience with data warehousing or lakehouse patterns, ingestion and transformation pipelines, data validation, and lineage practices in Azure SQL, Synapse, Microsoft Fabric, Power BI dataflows, or comparable platforms.
- Demonstrated proficiency in Microsoft modern workplace governance: Microsoft Purview Data Loss Prevention, Microsoft Entra ID, Conditional Access, Microsoft Graph API, and Managed Environments for Power Platform.
- Strong working knowledge of Microsoft Power Platform: Power Automate, Power Apps, custom connectors, AI Builder, and Power Platform DLP policies.
- Programming proficiency in Python with working fluency in PowerShell, SQL (including T\-SQL), and KQL.
- Experience implementing identity\-aware data access patterns, including row\-level security and least\-privilege design at the data layer.
- Solid understanding of HIPAA Privacy and Security Rules, PHI and PII handling, de\-identification techniques, and Business Associate Agreement scoping for AI services.
- Demonstrated ability to write and maintain technical documentation: architecture diagrams, data flows, runbooks, and audit\-readiness artifacts.
- Working knowledge of Responsible AI practices: bias and fairness considerations, evaluation methodology, guardrails, transparency, and human\-in\-the\-loop design.
- Experience working in a Managed Service Provider (MSP), consulting, or shared\-services environment serving multiple client organizations.
- Ability to maintain high level of confidentiality.
- Current driver’s license, acceptable driving record and proof of adequate insurance required.
Preferred:
- Direct experience in a HIPAA\-covered entity or business associate with hands\-on responsibility for AI services governance
- Experience in a Managed Care Organization (MCO), payer, behavioral health, or comparable regulated healthcare environment
- Familiarity with the Health Sector Coordinating Council (HSCC) Health Industry AI Cyber Governance Framework, or comparable sector\-specific AI governance guidance
- Experience leading or contributing to a Data Readiness Assessment, Master Data Management initiative, or Data Stewardship program
- Hands\-on experience with MLOps tooling and practice (MLflow or equivalent, Docker, CI/CD for ML, drift detection and monitoring)
- Microsoft certifications relevant to the role (Azure AI Engineer Associate, Microsoft Certified: Power Platform Developer, Microsoft Certified: Azure Data Engineer Associate, Microsoft Applied Skills credentials, or comparable)
- Prior experience as a client\-side counterpart to a strategic consulting engagement
- Familiarity with vendor evaluation discipline: ability to assess AI vendor pitches against model provenance, data residency, BAA scope, and integration feasibility
- Mentoring or coaching experience working with technical peers in an informal capacity
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 Lakeland Care, Inc., 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. Mid-level AI roles across all categories have a median of $200,000.
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.
Lakeland Care, Inc. AI Hiring
Lakeland Care, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fond du Lac, WI, US.
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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