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About This Role
Overview:
The AI Engineer V (Principal) is the most senior individual-contributor technical authority for artificial intelligence and machine learning within AssistRx Engineering. Reporting to the Vice President, Engineering, this role defines and drives the company’s AI technical strategy and leads the design, build, and deployment of production-grade AI solutions across the specialty medication access ecosystem, including large language model applications, predictive models, intelligent document processing, and agentic automation that improve speed, accuracy, and scale in patient onboarding, benefits verification, prior authorization, document intake, and patient and provider engagement.
The AI Engineer V (Principal) shapes AI direction at the enterprise level, solves the company’s most complex and ambiguous technical problems, and sets the architecture, evaluation standards, and responsible AI practices for all AI work at AssistRx. The role serves as a trusted advisor to engineering and executive leadership, represents AssistRx’s AI capabilities with clients, partners, and auditors, and develops the next generation of AI technical leaders. The Principal AI Engineer partners closely with Software Engineering, Product, Architecture, Data, Security, Compliance, and Client Services, and is accountable for the quality, performance, cost efficiency, and safe and compliant use of AI with protected health information across the enterprise. Responsibilities: AI Strategy & Solution Design* Define the multi-year AI technical strategy and roadmap in partnership with the Vice President, Engineering, Product, and Architecture, aligning AI investments to business value, feasibility, and risk
- Lead the design of the most complex enterprise-scale AI and machine learning solutions, including generative AI, predictive models, and intelligent document processing that span multiple platforms, products, and clients
- Architect large language model applications using retrieval augmented generation, prompt engineering, structured outputs, function and tool calling, and agent-based orchestration
- Set the patterns for agentic workflows, including task decomposition, tool and function calling, memory and state management, routing, clear boundaries, human review checkpoints, and fallback paths for high-impact decisions such as benefits verification and prior authorization
- Identify new AI opportunities and emerging technologies that create competitive advantage, and lead proofs of concept that move proven pilots to scale
- Determine the right approach for each problem, including prompting, retrieval augmentation, tool use, task-specific models, and conventional software or deterministic logic when a model is not the right answer
- Set the standard for design documentation of AI features, covering intended behavior, context and retrieval design, prompt and agent structure, evaluation approach, and fallback behavior
AI Foundations & Platform* Own the enterprise AI reference architecture, platform strategy, and engineering standards, including model access, prompt and version management, and retrieval services, and ensure their adoption across all engineering teams
- Lead evaluation of commercial models, AI platforms, and vendor tools, and make build-versus-buy recommendations to executive leadership based on quality, cost, security, strategic fit, and time to value
- Guide vendor and cloud provider relationships from a technical perspective, including model selection, capacity, pricing, and risk
- Anticipate scaling, reliability, and cost challenges in AI systems, and lead architectural investments that address them before they affect clients
Model Development, Evaluation & Optimization* Set organization-wide targets for accuracy, groundedness, hallucination rate, task completion, latency, and cost, and guide model selection, tuning, and evaluation against them
- Define the evaluation strategy, benchmark datasets, and automated test frameworks used across the organization to measure output quality and detect regressions before each release
- Guide optimization of retrieval pipelines, embeddings, chunking strategies, and inference performance to improve quality and reduce cost per transaction at scale
- Direct error analysis and human-in-the-loop review programs with operations and clinical subject matter experts, and ensure findings drive improvement of prompts, models, and guardrails
- Establish the experimentation approach, including A/B testing across prompts, retrieval configurations, model selection, and workflow design
- Define and enforce quality gates and regression thresholds so AI features must meet defined accuracy and safety criteria before release
Data Engineering & Integration* Set the technical direction for the pipelines that prepare structured and unstructured healthcare data, such as enrollment forms, payer documents, and clinical notes, for training, retrieval, and inference
- Partner with Data Engineering and Architecture to define enterprise data contracts, feature sets, vector stores, and lineage for AI workloads
- Define enterprise standards for de-identification, masking, and data minimization when working with protected health information
Machine Learning Operations & Production Support* Define enterprise standards for CI/CD, source control, containerization, and infrastructure-as-code practices for AI workloads
- Establish versioning, promotion, and rollback practices for prompts, agents, and model configurations, treating them as versioned artifacts alongside application code
- Set the observability strategy for accuracy drift, groundedness, task completion, token consumption, latency, cost, and reliability, including alerting and incident response practices
- Own the strategy for model and inference spend, applying caching, routing, and model right-sizing to keep AI solutions cost-effective at scale
- Serve as the final technical escalation point for critical production issues and client-reported defects in AI-enabled features, and drive systemic improvements that prevent recurrence
Responsible AI, Security, Privacy & Compliance* Lead the company’s responsible AI approach and ensure solutions satisfy healthcare privacy, data security, and regulatory requirements, including HIPAA and HITRUST
- Partner with Security, Compliance, and Legal to address AI-specific risks such as prompt injection, data leakage, and insecure output handling, and to confirm vendor agreements (including Business Associate Agreements) are in place before PHI is processed
- Define guardrails, content filtering, human oversight, and audit trails appropriate to the risk profile of each use case
- Contribute to internal AI governance, and represent AssistRx’s AI practices in audits, client due diligence, and security reviews
- Track emerging AI regulation and industry guidance, and translate it into engineering policy and practice
Technical Leadership, Influence & Enablement* Serve as a trusted technical advisor to the Vice President, Engineering and executive leadership on AI strategy, risk, investment, and capability
- Provide technical leadership across the engineering organization, influencing architecture and engineering direction at the enterprise level without direct authority
- Mentor and develop Staff, Senior, and mid-level engineers, build the AI technical bench, and raise AI fluency across engineering and the broader company
- Lead cross-functional technical initiatives and resolve complex technical disagreements across teams
- Work with Product Management, Client Services, and business leaders to identify high-value AI use cases and define clear success measures before build begins
- Communicate AI capabilities, limitations, risks, and results in plain language to technical and non-technical audiences, including executives, clients, and external partners
- Contribute to hiring strategy, interviewing, and role definition for AI and related engineering roles
- Advance AI-assisted and agentic development practices across the engineering organization, including specification-driven workflows and agentic coding tools, and share what works with engineering peers
- Participate in agile team activities as needed, including sprint reviews and retrospectives, and conduct regular self-guided study to stay at the forefront of a fast-moving AI landscape
Qualifications:
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related technical field (or equivalent experience); Master’s degree preferred
- 10+ years of software engineering experience, including 6+ years building and deploying AI or machine learning solutions in production
- Track record of leading the strategy and delivery of enterprise-scale AI solutions used by real customers or operations teams, with significant, measurable business outcomes
- Expert programming skills in Python, along with experience in .NET/C#/Java or a comparable enterprise language, including building backend services and REST APIs (for example FastAPI, Flask, or ASP.NET)
- Deep, hands-on expertise with large language models and generative AI patterns, including retrieval augmented generation, prompt engineering, embeddings, vector and hybrid search, reranking, structured outputs, function and tool calling, context window management, and model evaluation
- Extensive experience architecting and operating production AI workloads on cloud-based AI and machine learning platforms (AWS preferred, including services such as Amazon Bedrock and Amazon SageMaker; equivalent Azure experience with Azure AI Foundry, Azure OpenAI Service, or Azure Machine Learning also acceptable)
- Expert knowledge of APIs, microservices, distributed systems, SQL, and modern development practices including Git, CI/CD, containerization, and automated testing
- Demonstrated ability to deliver in an environment where data privacy, security, and regulatory requirements shape solution design
- Demonstrated enterprise-level technical leadership, including setting technical strategy, driving architectural decisions across teams, and developing other technical leaders
- Ability to work independently on highly ambiguous, high-impact problems, set direction, and make sound tradeoffs with limited precedent
- Hands-on experience building software with agentic coding tools such as Claude Code, GitHub Copilot agents, Codex, or Cursor, including specification-driven workflows and multi-step agent execution
- Deep practical understanding of how large language models behave in production, including hallucination, prompt sensitivity, non-determinism, and latency and cost tradeoffs
- Expert knowledge of evaluation methods for LLM systems, including automated scoring, LLM-as-judge techniques, and human review workflows
- Exceptional written and verbal communication skills, with the ability to influence executives, clients, and engineers and to represent the company’s AI approach externally
PREFERRED QUALIFICATIONS* Master’s or doctoral degree in Computer Science, Machine Learning, Data Science, or related field
- Experience in healthcare technology, specialty pharmacy, HUB services, ePA, or EHR/EMR integrations
- Experience with intelligent document processing, OCR, and data extraction from unstructured enrollment or clinical documents
- Applied machine learning or data science background, including experiment design, model evaluation, and error analysis
- Experience with Docker, Kubernetes, or infrastructure-as-code at scale
- Experience with agent and orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, Strands, or Model Context Protocol
- Experience with machine learning operations tooling, model registries, feature stores, and LLM observability or evaluation platforms
- Experience with model fine-tuning or smaller task-specific models for classification and extraction
- Familiarity with AI security guidance such as the OWASP Top 10 for LLM Applications
- Familiarity with HIPAA, HITRUST, or responsible AI frameworks such as the NIST AI Risk Management Framework
- Prior experience establishing an AI function or serving as the senior AI technical leader for an organization
- Experience representing technical capabilities in client due diligence, security reviews, or external audits
COMPETENCIES* Technical Depth
- Strategic Thinking
- Analytical Rigor & Problem Solving
- Experimentation & Measurable Outcomes
- Accuracy & Quality
- Sound Judgment & Risk Awareness
- Ownership & Accountability
- Pragmatism & Bias for Delivery
- Communication
- Collaboration & Influence
- Technical Leadership & Talent Development
- Curiosity & Learning Agility
- Adaptability
- Initiative & Innovation
- Ethics & Integrity
Pay Range: USD $162,239.00 - USD $204,048.00 /Yr.
Salary Context
This $162K-$204K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At AssistRx, 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($183K) sits 14% below the category median. Disclosed range: $162K to $204K.
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.
AssistRx AI Hiring
AssistRx has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in FL, US. Compensation range: $168K - $204K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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.
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