Sr AI Platform Engineer

Honeywell•Phoenix, AZ

2h•Hybrid

About The Position

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.

Requirements

5 or more years of experience in platform engineering, cloud engineering, MLOps, DevOps, or a related technical discipline.
Strong hands-on experience with AWS services such as IAM, VPC, S3, Lambda, ECS/EKS, Step Functions, CloudWatch, and networking/security best practices.
Practical experience implementing and supporting AI or ML platforms , including compute environments, containerization, and production model or LLM service deployment.
Experience with Databricks , including workspace configuration, cluster/pool setup, Unity Catalog, Delta Lake, and integration with enterprise identity and governance.
Working knowledge of Snowflake architecture, storage/compute separation, security, and integration with AI workflows.
Experience with Dataiku for automation (Scenarios), environment setup, execution engines, and project-level governance.
Proficiency with Infrastructure-as-Code , ideally Terraform, CLI-based provisioning, and Git-based workflow automation.
Strong understanding of security fundamentals—least privilege, tokenization/secrets, data access controls, network segmentation, and auditability.
Ability to collaborate with cross-functional AI teams and translate architectural guidance into robust platform implementations.

Nice To Haves

Experience supporting diverse AI workloads: classic ML pipelines , GenAI/LLM applications , and agentic AI systems that use tools, memory, vector search, or multi-step reasoning.
Knowledge of vector databases, semantic search, retrieval frameworks, and embedding compute patterns (e.g. Databricks Vector Search, OpenSearch, Milvus, Pinecone).
Familiarity with MLOps practices—CI/CD for ML, model registries (MLflow), evaluation pipelines, monitoring, and incident workflows.
Hands-on experience with IaC modules for Databricks, Snowflake, or hybrid cloud AI architectures.
Understanding of multi-agent orchestration patterns, event-driven AI workflows, and safe/guarded execution environments.
Strong problem‑solving skills, attention to detail, and focus on platform reliability and scalability.
Passion for emerging AI technologies, platform innovation, and building developer‑friendly infrastructure that accelerates AI delivery.

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.