What are the responsibilities and job description for the Sr AI Platform Engineer position at Honeywell?
Sr AI Platform Engineer
As a Sr AI Platform here at Honeywell, you will play a crucial role in supporting AI solutions that drive business insights, enhance decision-making processes and empower AI solutions. Your expertise will help in critical data science development activities across all AI modalities (classic, Gen and agentic) and data types (structured and unstructured).
You will report directly to our AI Director, and you’ll work out of our Phoenix, AZ or Charlotte, NC location on a hybrid work schedule.
In this role, you will impact the organization by leveraging your technical skills to develop innovative data solutions that support strategic initiatives and improve operational efficiency.
KEY RESPONSIBILITIES
- Design, build, and maintain the core AI platform infrastructure that supports classic machine learning, GenAI/LLM workloads, and emerging agentic AI systems.
- Implement and manage cloud‑native environments in AWS, including compute, networking, IAM, security controls, and serverless or containerized runtimes for AI workloads.
- Build scalable data and model infrastructure across Snowflake, Databricks (Delta Lake, Unity Catalog), and Dataiku, enabling unified governance, observability, lineage, and automation.
- Develop Infrastructure‑as‑Code (IaC) modules, environment templates, and reusable platform components to accelerate AI solution delivery.
- Deploy and operationalize vector databases, embedding pipelines, orchestration frameworks, and retrieval systems to support RAG and agentic AI architectures.
- Partner with Data Engineers, ML Engineers, MLOps, and Architects to deliver secure, reliable, high‑performance AI environments and production runtimes.
- Implement monitoring, alerting, logging, and cost‑optimization frameworks for all AI platform services, ensuring stability and operational excellence.
- Support environment provisioning, workspace configuration, cluster management, CI/CD integration, and platform‑level testing required for scalable AI deployment.
- Ensure compliance with enterprise security, data governance, identity standards, and responsible AI guidelines across all AI modalities.