AI/ML Engineer
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Key skills for this role
About the Role
Cleveland Clinic Abu Dhabi seeks an AI/ML Engineer to design, build, deploy, and operate production-grade AI, machine learning, generative AI, computer vision, and agentic workflow solutions for healthcare.
Key Skills for This Role
Responsibilities
- 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
- Build reusable platform capabilities for experiment tracking, feature stores, vector stores, model registries, artifact repositories, data validation, model evaluation and governed deployment
- Containerize, serve and scale ML, deep learning, vision and GenAI models using production APIs and model serving frameworks
- Engineer reliable ML ready data and feature pipelines using lakehouse, Spark, SQL, streaming and event driven tools
- Deploy and operate vision models for medical imaging, digital pathology, clinical video, document images and operational image/video streams
- 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
- Implement Git based workflows, peer review, automated tests, CI/CD pipelines, model registry controls, model/prompt/data tests, vulnerability scans and release gates
- Develop APIs, microservices, SDKs, webhooks and integration services that embed AI outputs into EHR, command center, operational, digital and analytics applications
- Implement secure SDLC, RBAC, managed identities, key management, network controls, PHI protections, de identification, audit logging and approved data retention practices
Requirements
- Bachelor degree in Computer Science, Software Engineering, Computer Engineering, Data Engineering, Artificial Intelligence, Data Science, Mathematics, Biomedical Engineering or a related technical field
- 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
- Master degree in Computer Science, AI/ML, Software Engineering, Data Engineering, Cloud Computing, Biomedical Informatics or a related technical discipline
- 5+ years in production AI/ML engineering or platform engineering; healthcare, life sciences or other regulated industry experience; experience leading reusable AI platform capabilities
Full Job Posting
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 service
PRIMARY JOB DUTIES AND RESPONSIBILITIES
- 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.
- 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.
- 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.
- 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.
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.
- Master degree in Computer Science, AI/ML, Software Engineering, Data Engineering, Cloud Computing, Biomedical Informatics or a related technical discipline (preferred).
- 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.
- 5+ years in production AI/ML engineering or platform engineering; healthcare, life sciences or other regulated industry experience; experience leading reusable AI platform capabilities (preferred).
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