Overview
The AI Engineer IV (Staff) is a senior technical leader within AssistRx Engineering and the company’s most experienced hands-on authority for artificial intelligence and machine learning. Reporting to the Vice President, Engineering, this role designs, builds, and deploys 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 IV (Staff) sets the technical direction for AI across multiple teams and products, owns the most complex and highest-risk AI solutions, and defines the architecture, evaluation standards, and responsible AI practices that other engineers build on. The role multiplies the impact of the engineering organization through technical leadership, mentorship, and reusable platforms, and partners closely with Software Engineering, Product, Architecture, Data, Security, Compliance, and Client Services to turn business problems into measurable outcomes. The AI Engineer IV (Staff) is accountable for solution quality, model performance, cost efficiency, and the safe and compliant use of AI with protected health information.

Responsibilities
AI Solution Design & Development

Lead the design, build, and deployment of complex, multi-system AI and machine learning solutions that advance AssistRx’s AI roadmap, including generative AI, predictive models, and intelligent document processing

Architect applications on large language models using retrieval augmented generation, prompt engineering, structured outputs, function and tool calling, and agent-based orchestration

Design agentic workflows with 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

Lead the design of APIs, services, and integrations that embed AI capabilities into existing AssistRx platforms and client-facing workflows, resolving cross-team technical dependencies

Translate ambiguous business and clinical workflow requirements into technical strategies, designs, and production releases, taking proven pilots to scale across products and clients

Select 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

Author and review design documentation for AI features covering intended behavior, context and retrieval design, prompt and agent structure, evaluation approach, and fallback behavior, setting the documentation standard for the organization

AI Foundations & Platform

Own the AI reference architecture, reusable components, and engineering standards for AI development at AssistRx, including model access, prompt and version management, and retrieval services, and drive their adoption across engineering teams

Evaluate commercial models, AI platforms, and vendor tools, and provide build-versus-buy recommendations to the Vice President, Engineering based on quality, cost, security, and time to value

Shape the AI technical roadmap in partnership with Product and Architecture, helping prioritize work by business value, feasibility, and risk, and maintaining the AI use case inventory and technical backlog

Identify platform gaps and technical debt in AI systems and lead the investments needed to close them

Model Development, Evaluation & Optimization

Select, tune, and evaluate models against defined targets for accuracy, groundedness, hallucination rate, task completion, latency, and cost

Define the organization’s evaluation frameworks, benchmark datasets, and automated test suites that measure output quality and detect regressions before each release

Optimize retrieval pipelines, embeddings, chunking strategies, and inference performance to improve quality and reduce cost per transaction





Lead error analysis and human-in-the-loop review cycles with operations and clinical subject matter experts, applying findings to improve prompts, models, and guardrails

Design and run structured experiments and A/B tests across prompts, retrieval configurations, model selection, and workflow design, and use the results to drive iteration

Establish and enforce quality gates and regression thresholds so AI features must meet defined accuracy and safety criteria before release

Data Engineering & Integration

Define and guide 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 to define data contracts, feature sets, vector stores, and lineage for AI workloads

Set standards for de-identification, masking, and data minimization when working with protected health information

Machine Learning Operations & Production Support

Set standards for CI/CD, source control, containerization, and infrastructure-as-code practices for AI workloads

Maintain versioning, promotion, and rollback practices for prompts, agents, and model configurations, treating them as versioned artifacts alongside application code

Define monitoring for accuracy drift, groundedness, task completion, token consumption, latency, cost, and reliability through logging, observability, and alerting

Track and manage model and inference spend, applying caching, routing, and model right-sizing to keep AI solutions cost-effective at scale

Serve as the escalation point for complex production issues and client-reported defects in AI-enabled features, leading root cause analysis and continuous improvement of deployed AI services

Responsible AI, Security, Privacy & Compliance

Build solutions that satisfy healthcare privacy, data security, and regulatory requirements, including HIPAA and HITRUST

Partner with Security and Compliance 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

Document model design, data sources, intended use, and known limitations to support internal AI governance, audits, and client due diligence

Technical Leadership & Collaboration

Provide technical leadership across multiple teams, influencing design decisions and engineering direction without direct authority

Mentor and coach Senior and mid-level engineers on AI development, code quality, evaluation, and design practices, and raise the overall level of AI fluency across engineering

Lead design reviews and peer code reviews, and contribute to shared standards and reference architectures across the engineering organization

Work with Product Management and business stakeholders 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 engineering leadership, executives, and clients

Participate in day-to-day agile team activities, including sprint planning, daily standups, sprint reviews, and retrospectives

Apply and help improve AI-assisted and agentic development practices within the engineering organization, including specification-driven workflows and agentic coding tools, and share what works with engineering peers

Conduct regular self-guided study to stay current in a fast-moving AI landscape, and bring relevant new techniques and tooling to the team’s attention





Qualifications

Bachelor’s degree in Computer Science, Engineering, Data Science, or related technical field (or equivalent experience)

7+ years of software engineering experience, including 4+ years building and deploying AI or machine learning solutions in production

Track record of leading and shipping multiple AI features used by real customers or operations teams, with measurable business outcomes

Strong 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)

Hands‑on experience 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

Experience designing and deploying 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)

Strong knowledge of APIs, microservices, SQL, and modern development practices including Git, CI/CD, containerization, and automated testing

Demonstrated ability to deliver in an environment where data privacy and security requirements shape solution design

Demonstrated technical leadership across teams, including setting technical direction, driving architectural decisions, and mentoring other engineers

Ability to work independently on ambiguous, high‑impact problems, set direction on technical approach, 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

Practical understanding of how large language models behave in production, including hallucination, prompt sensitivity, non‑determinism, and latency and cost tradeoffs

Solid knowledge of evaluation methods for LLM systems, including automated scoring, LLM‑as‑judge techniques, and human review workflows

Excellent written and verbal communication skills, with the ability to influence and collaborate effectively with engineers, executives, and clients in a fast‑paced agile environment

Preferred Qualifications

Master’s 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

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 as an early or lead AI engineer helping an organization adopt AI

Competencies

Technical Depth

Analytical Rigor & Problem Solving

Experimentation & Measurable Outcomes

Accuracy & Quality

Sound Judgment & Risk Awareness

Ownership & Accountability

Pragmatism & Bias for Delivery

Communication

Collaboration & Influence

Technical Leadership & Mentorship

Curiosity & Learning Agility

Adaptability

Initiative & Innovation

Ethics & Integrity

Pay Range
USD $135,051.00 - USD $168,814.00 /Yr.

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📌 AI Engineer IV (Staff) (Manitoba)
🏢 AssistRx
📍 Manitoba