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About This Role
Position Summary:
Magpie Health Analytics is seeking an experienced AI Engineer to design, build, and operate secure, trustworthy generative AI solutions that help our healthcare clients deliver faster, more consistent access to authoritative program guidance.
Key Responsibilities:
Our clients are government health payers. Your primary responsibility is to design, develop, test, deploy, and maintain an AI-enabled decision-support capability that allows users to search and navigate large bodies of program documentation (statutes, regulations, rules, and guidance) using natural language. The solution will first support internal customers and will later be extended to external, public-facing users. You will work in collaboration with project managers, data engineers, full stack developers, analysts, and client stakeholders to ensure the successful implementation of the client solution. The AI Engineer duties include:
- Design, develop, and maintain generative AI solutions that ground large language model (LLM) responses in authoritative source documentation.
- Build and maintain document ingestion pipelines, including parsing, chunking, metadata tagging, embedding generation, and vector/semantic search indexing.
- Collaborate with cross-functional teams and subject matter experts to understand user needs and translate them into requirements, prompts, and evaluation criteria.
- Implement responses with source citations and direct links to the underlying documentation to ensure traceability.
- Design and implement guardrails, including content filtering, scope restrictions, PII/PHI protections, and refusal handling for out-of-scope questions.
- Develop evaluation frameworks and test sets to measure retrieval quality, answer accuracy, groundedness, and consistency; monitor and tune performance over time.
- Deploy and operate solutions within approved AWS services and federal security controls, supporting documentation needed for security authorization and AI governance reviews.
- Implement logging, monitoring, and audit trails to support transparency, cost management, and responsible AI oversight.
- Write clean, reusable, and well-documented code, and participate in code reviews, testing, and deployment processes.
- Stay up to date with emerging AI technologies, models, and federal AI guidance.
Educational Requirements:
- A master's degree in computer science, data science, engineering, or another relevant field.
Qualifications:
- 5+ years of experience developing software and cloud-based solutions, with demonstrated recent experience designing and deploying LLM or generative AI applications.
- Strong proficiency in Python and experience with modern LLM APIs, SDKs, orchestration frameworks, and AI/ML libraries (e.g., Hugging Face, LangChain, LlamaIndex, or equivalent).
- Hands-on experience designing and deploying generative AI solutions, including embeddings, vector databases, and hybrid/semantic search.
- Experience with AWS AI and data services such as Amazon Bedrock, Amazon OpenSearch, Amazon Kendra, SageMaker, Lambda, or S3.
- Experience with prompt and context engineering, LLM evaluation, and techniques for reducing hallucinations and ensuring answers are grounded in source material.
- Designing AI applications to support multiple foundation models/providers and evaluating models based on accuracy, latency, cost, security, and use-case requirements.
- Experience with Agile software development methodologies.
- Experience with building and maintaining CI/CD pipelines, testing, and deploying infrastructure using services such as GitHub Actions, CloudFormation, Terraform, and/or Jenkins.
- Excellent problem-solving and communication skills, including the ability to explain AI capabilities and limitations to non-technical stakeholders.
- Ability to work independently and in a team, with strong attention to detail and presentation.
Preferred Qualifications:
- Experience supporting federal health programs, including program policy, regulatory guidance, and customer or stakeholder support operations.
- Experience deploying AI solutions in federal environments, including FedRAMP-authorized services, ATO/security authorization processes, and NIST 800-53 controls.
- Familiarity with responsible AI and federal AI governance frameworks (e.g., NIST AI Risk Management Framework, OMB AI guidance).
- Understanding of security and compliance regulations in healthcare, such as HIPAA and HITECH.
- Experience building conversational or search interfaces, including Section 508 accessibility compliance.
- Relevant AWS certification (e.g., AWS Certified Machine Learning Engineer – Associate, AI Practitioner, Generative AI Developer – Professional, Solutions Architect, or equivalent)
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Magpie Health Analytics, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Magpie Health Analytics AI Hiring
Magpie Health Analytics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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