AI/ML Engineer 

JOB SUMMARY

The AI/ML Engineer designs, builds, deploys and operates production-grade AI, machine learning, generative AI, computer vision and agentic workflow solutions for CCAD. The role converts data-science prototypes and clinical innovation ideas into secure, scalable, observable and supportable AI services integrated with CCAD’s data platforms, EHR, PACS/RIS, command-center workflows and digital applications. As CCAD advances toward the north star of an autonomous hospital, the AI/ML Engineer owns the engineering patterns, MLOps/LLMOps, model serving, cloud infrastructure, integration, monitoring and reliability practices required to run AI safely at healthcare scale.

ROLE INTERACTS DIRECTLY WITH

TITLE/DEPARTMENT Data Scientists and Applied AI Researchers

FREQUENCY Often

TYPE OF INTERACTION Productionization, model packaging, evaluation automation, deployment design and monitoring

ROLE INTERACTS DIRECTLY WITH

TITLE/DEPARTMENT Clinical Leaders, Physicians, Nurses, Allied Health SMEs

FREQUENCY Often

TYPE OF INTERACTION Workflow integration, clinical safety controls, user acceptance and production feedback

ROLE INTERACTS DIRECTLY WITH

TITLE/DEPARTMENT Product Owners / Operational Leaders

FREQUENCY Often

TYPE OF INTERACTION Requirements, roadmap planning, benefits realization, user experience and adoption

ROLE INTERACTS DIRECTLY WITH

TITLE/DEPARTMENT Data Engineers / Platform Engineers

FREQUENCY Often

TYPE OF INTERACTION Data pipelines, feature stores, lakehouse services, streaming data and data-quality contracts

ROLE INTERACTS DIRECTLY WITH

TITLE/DEPARTMENT Enterprise Architecture / Cloud / Infrastructure

FREQUENCY Often

TYPE OF INTERACTION Reference architecture, hosting, network, GPU/compute, storage, cost and scalability

ROLE INTERACTS DIRECTLY WITH

TITLE/DEPARTMENT Clinical Informatics / Epic Teams

FREQUENCY Often

TYPE OF INTERACTION EHR integration, clinical decision support, APIs, Epic Clarity/Caboodle/Cogito data products

ROLE INTERACTS DIRECTLY WITH

TITLE/DEPARTMENT PACS/RIS, Imaging, Pathology and Biomedical Engineering

FREQUENCY As needed

TYPE OF INTERACTION DICOM/DICOMweb workflows, imaging pipelines, device/IoMT and edge/video integration

ROLE INTERACTS DIRECTLY WITH

TITLE/DEPARTMENT Cybersecurity, Privacy, Compliance and Data Governance

FREQUENCY Often

TYPE OF INTERACTION Secure SDLC, PHI controls, audit logging, RBAC, model governance and regulatory readiness

ROLE INTERACTS DIRECTLY WITH

TITLE/DEPARTMENT Vendors and Strategic Partners

FREQUENCY As needed

TYPE OF INTERACTION Platform integration, technical due diligence, implementation, support and service management

PRIMARY JOB DUTIES AND RESPONSIBILITIES

Area - AI Solution Architecture & Production Design

What You'll Do:

• Translate data-science prototypes and clinical innovation concepts into robust, secure, scalable and maintainable production AI services.

• Design batch, real-time, streaming, event-driven, API-based and edge AI architectures that support autonomous hospital workflows such as patient flow, command centers, diagnostics, capacity optimization and digital front door automation.

• Define data contracts, inference patterns, latency/throughput targets, failure modes, fallback paths and human approval controls for safety-critical workflows.

Area - ML Platform Engineering

What You'll Do:

• Build reusable platform capabilities for experiment tracking, feature stores, vector stores, model registries, artifact repositories, data validation, model evaluation and governed deployment.

• Develop standardized templates, reference architectures, SDKs and CI/CD patterns that accelerate AI delivery across CCAD.

• Implement infrastructure as code, environment management, secrets management, access controls and cost controls for AI workloads.

Area - Model Serving, Inference & Optimization

What You'll Do:

• Containerize, serve and scale ML, deep learning, vision and GenAI models using production APIs and model-serving frameworks.

• Optimize latency, throughput, memory, GPU utilization and cost using batching, caching, quantization, distillation, async processing and autoscaling where appropriate.

• Design canary, blue/green, shadow and rollback deployment patterns for models, prompts, retrieval pipelines and agents.

Area - Data, Feature & Streaming Pipelines

What You'll Do:

• Engineer reliable ML-ready data and feature pipelines using lakehouse, Spark, SQL, streaming and event-driven tools in partnership with Data Engineering.

• Implement schema validation, data quality checks, data lineage, reproducibility, de-identification and monitoring for data feeding AI services.

• Integrate with Epic Clarity/Caboodle/Cogito, FHIR, HL7, DICOM/DICOMweb, OMOP, PACS/RIS, devices/IoMT and approved enterprise APIs.

Area - Computer Vision & Multimodal Engineering

What You'll Do:

• Deploy and operate vision models for medical imaging, digital pathology, clinical video, document images and operational image/video streams.

• Build DICOM/DICOMweb and PACS/RIS integration patterns, image preprocessing, tiling, segmentation pipelines, annotation workflows, explainability overlays and GPU/edge inference services.

• Engineer multimodal APIs and workflows using CNNs, U-Net, Vision Transformers, object detection, segmentation, MONAI, OpenCV, NVIDIA/Triton tooling and vision-language models where appropriate.

Area - LLMOps, RAG & Agentic AI Engineering

What You'll Do:

• Build secure LLM applications using Azure OpenAI/OpenAI, Azure AI Foundry, approved open-source models, RAG, embeddings, vector search, knowledge graphs and tool/function calling.

• Engineer prompt/version management, retrieval pipelines, evaluation harnesses, guardrails, content filters, structured output validation, audit logs and prompt-injection defenses.

• Connect agents to approved tools, data sources and workflows with role-based access, human approvals and operational monitoring.

Area - MLOps, CI/CD & Reliability Engineering

What You'll Do:

• Implement Git-based workflows, peer review, automated tests, CI/CD pipelines, model registry controls, model/prompt/data tests, vulnerability scans and release gates.

• Monitor data drift, concept drift, model performance, fairness, hallucination/grounding, latency, service health, cost and user feedback.

• Create runbooks, dashboards, alerts, incident response procedures, retraining/redeployment triggers and service-level objectives for production AI services.

Area - Application Integration & Developer Experience

What You'll Do:

• Develop APIs, microservices, SDKs, webhooks and integration services that embed AI outputs into EHR, command-center, operational, digital and analytics applications.

• Partner with UI/UX, BI and application teams to expose model results, explanations, feedback loops and human-in-the-loop controls.

• Improve developer productivity through reusable libraries, templates, documentation, sample applications and internal enablement.

Area - Security, Privacy & Responsible AI Operations

What You'll Do:

• Implement secure SDLC, RBAC, managed identities, key management, network controls, PHI protections, de-identification, audit logging and approved data retention practices.

• Operationalize model cards, risk controls, model inventories, validation artifacts, monitoring evidence and change-control records for governed AI systems.

• Work with privacy, compliance, cybersecurity and clinical governance stakeholders to ensure AI services meet CCAD policies and healthcare regulatory expectations.

Area - Agile Delivery & Production Support

What You'll Do:

• Work in Agile pods with Data Scientists, clinicians, product owners, data engineers, cloud engineers and governance teams to deliver measurable AI products.

• Provide production support, root-cause analysis, reliability improvements and technical escalation for AI services.

• Mentor teammates on ML engineering, MLOps/LLMOps, secure coding, cloud architecture and responsible AI engineering practices.

QUALIFICATION REQUIREMENTS

Bachelor degree in Computer Science, Software Engineering, Computer Engineering, Data Engineering, Artificial Intelligence, Data Science, Mathematics, Biomedical Engineering or a related technical field.

PREFERRED

Master degree in Computer Science, AI/ML, Software Engineering, Data Engineering, Cloud Computing, Biomedical Informatics or a related technical discipline.

EXPERIENCE REQUIREMENTS

3+ years in software engineering, ML engineering, platform engineering, data engineering or cloud engineering, including 3+ years building, deploying or operating production ML/AI/GenAI solutions.

PREFERRED

5+ years in production AI/ML engineering or platform engineering; healthcare, life sciences or other regulated-industry experience; experience leading reusable AI platform capabilities.