AI Engineer

Our Promise To You

Joining Advent Health is about being part of something bigger. It’s about belonging to a community that believes in the wholeness of each person, and serves to uplift others in body, mind and spirit. Advent Health is a place where you can thrive professionally, and grow spiritually, by Extending the Healing Ministry of Christ. Where you will be valued for who you are and the unique experiences you bring to our purpose-minded team.

All while understanding that together we are even better.

• Benefits from Day One:
  Medical, Dental, Vision Insurance, Life Insurance, Disability Insurance
• Paid Time Off from Day One
• 403-B Retirement Plan
• 4 Weeks 100% Paid Parental Leave
• Career Development
• Whole Person Well-being Resources
• Mental Health Resources and Support
• Pet Benefits

Full time

Day (United States of America)

602 COURTLAND ST

ORLANDO

Florida

32804

Design and implement custom LLM workflows including prompt engineering, model fine-tuning, instruction tuning, and retrieval-augmented generation (RAG) to meet enterprise-specific requirements. Own the full lifecycle of AI models from experimentation to deployment, monitoring, versioning, and continuous improvement using industry-standard MLOps practices. Design and develop comprehensive technical plans and system architectures that effectively address identified problems and proposed AI-driven solutions, ensuring scalability, maintainability, and alignment with organizational objectives.

Build and maintain scalable AI/ML infrastructure including model registries, vector databases, embedding stores, experiment tracking tools, and inference pipelines. Evaluate, integrate, and deploy foundation models (commercial and open-source) into production environments with clear performance, cost, and privacy tradeoff analysis. Architect and implement cloud-native ML systems using platforms such as Azure ML, AWS Sage Maker, or GCP Vertex AI; containerize and deploy models using tools like Docker, Kubernetes, or serverless frameworks.

Work closely with data scientists, Dev Ops, software engineers, and business stakeholders to integrate AI models into existing applications and services with reliable APIs and monitoring. Design and develop secure, scalable middleware solutions and APIs to integrate enterprise systems with large language models and AI services. Leverage cloud-native technologies to enable seamless orchestration of data and model workflows across distributed environments.

Implement cloud infrastructure using Infrastructure-as-Code (IaC) principles with tools such as Terraform and Bicep. Automate deployment of secure, compliant, and cost-optimized cloud resources to support AI model serving, vector stores, and data pipelines. Enforce enterprise-grade security protocols across AI workflows, including access control, secret management, and policy-as-code. Ensure compliance with organizational and regulatory standards when integrating AI capabilities into production systems.

Establish observability frameworks to monitor latency, throughput, drift, accuracy, and resource consumption. Continuously optimize models for performance, efficiency, and user impact. Ensure models comply with ethical AI principles, data privacy regulations, and organizational governance frameworks. Document model behavior, limitations, and evaluation benchmarks. Stay up to date with advancements in AI algorithms, frameworks, natural language processing (NLP), and large language models (LLMs) to recommend innovative solutions.

• Strong experience with transformer-based architectures (e.g., GPT, LLaMA, Mistral, Claude) and LLM customization techniques (LoRA, PEFT, instruction tuning, prompt chaining). [Required]
• Proficiency in Python, ML frameworks (e.g., PyTorch, Tensor Flow), and model management libraries (e.g., Hugging Face Transformers, Lang Chain, OpenLLM). [Required]
• Expertise in deploying ML models into production using CI/CD pipelines, Docker, Kubernetes, and cloud services (Azure, AWS, or GCP). [Required]
• Knowledge of vector databases (e.g., FAISS, Pinecone, Weaviate) and RAG pipelines for enterprise search and contextualization. [Required]
• Experience with MLOps platforms like MLflow, Weights & Biases, Sage Maker, or Azure ML for experiment tracking and model lifecycle orchestration. [Required]
• Understanding of data pipelines, ETL/ELT practices, and feature store integration in AI systems. [Required]
• Ability to evaluate trade-offs between performance, cost, latency, and explainability in real-world AI systems. [Required]
• Excellent written and verbal communication skills with the ability to document systems and present findings to technical and non-technical audiences. [Required]
• Ability to quickly learn, experiment, and iterate on AI-driven strategies. [Required]
• Experience deploying LLMs in enterprise or regulated environments (e.g., healthcare, finance, government).…