About this opportunity
ClifyX lists this Generative AI Engineer opportunity in phoenix, Arizona. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Must have SI experience with larger IT service provider
10+ years of experience in software architecture or engineering, with at least 5+ years in AI/ML specifically.
Proven experience designing and developing multi-agent AI systems in a production environment.
Significant experience in the healthcare industry, with a deep understanding of clinical workflows, RCM, data standards (HL7, FHIR), and regulated environments.
Technical skills
Expertise in multi-agent orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen).
Deep knowledge of LLM architectures, RAG implementation, and techniques for fine-tuning models.
Extensive experience with cloud platforms (AWS, Azure, or GCP) and related AI services.
Strong background in data engineering, including building ETL pipelines and managing vector stores.
Proficiency in Python and relevant AI/ML libraries (e.g., PyTorch, TensorFlow).
Hands-on experience with MLOps practices and tools (e.g., Docker, Kubernetes, MLflow).
System architecture
Define the architectural vision and strategy for agentic AI solutions, designing end-to-end architectures that include model integration, orchestration frameworks, memory systems, and tool-use capabilities.
Technical leadership
Guide and mentor cross-functional teams of AI engineers, data scientists, and DevOps specialists on architectural patterns and best practices for building scalable and reliable agentic AI systems.
Cloud infrastructure and MLOps
Design and deploy multi-agent AI systems on cloud platforms (AWS, Azure, or GCP), building and managing cloud-native AI pipelines with MLOps best practices for monitoring, evaluating, and scaling agents.
Healthcare integration
Lead the integration of agentic AI solutions with existing healthcare systems, and other enterprise platforms, while ensuring data interoperability and security.
Responsible AI
Ensure the implementation of strong AI governance, security, and ethical practices throughout the agent lifecycle, including bias mitigation, fairness checks, and compliance with healthcare regulations like HIPAA.
Proof of concept and scaling
Lead proof-of-concept (PoC) initiatives to validate new agentic capabilities, then develop strategies to scale successful prototypes into production-ready systems.
Technology evaluation
Evaluate and integrate a wide range of open-source and proprietary AI tools and technologies, including vector databases, orchestration frameworks (e.g., LangChain, CrewAI), and cloud-native AI services.
Thought leadership
Stay current with the latest advancements in agentic AI, generative models, and multi-agent frameworks, driving innovation within the company and potentially presenting at industry conferences.
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Worksite address
phoenix, AZ, 85003, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.