Senior Platform Engineer helping design, scale, and harden Pivotal’s AI - driven platform. Collaborating closely with engineering teams to improve reliability, security, and scalability.
About the role
- Senior AI Platform Engineer at Honeywell supporting AI solutions for enhanced decision-making in Phoenix, AZ or Charlotte, NC with hybrid work schedule.
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.
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.
Benefits
- employer-subsidized Medical, Dental, Vision, and Life Insurance
- Short-Term and Long-Term Disability
- 401(k) match
- Flexible Spending Accounts
- Health Savings Accounts
- EAP
- Educational Assistance
- Parental Leave
- Paid Time Off (for vacation, personal business, sick time, and parental leave)
- 12 Paid Holidays
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
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