AI Automation Engineer, Ping Identity

• 3–5 years of hands-on software engineering or data engineering experience, with a track record of delivering production systems — not just prototypes
• Proficiency in Python — you write clean, maintainable code, are comfortable working in data and AI engineering contexts, and actively use coding assistants (e.g., GitHub Copilot, Cursor, or similar) as part of your development workflow
• Experience building on Databricks — specifically with AgentBricks and building custom agents on the Databricks platform. You know your way around notebooks, Delta Lake, and production-grade agent deployment
• Demonstrated proficiency in building AI agents — you've designed, built, and shipped production agents (Glean canvas, LangChain, LlamaIndex, or similar), and you understand the engineering tradeoffs involved (retrieval, tool use, evaluation, latency, cost). This is a core expectation of the role, not a nice-to-have
• Proficiency with agentic coding frameworks (e.g., DSPy, LangChain, CrewAI, or similar) — you've used these in production contexts, not just tutorials, and know how to evaluate the right tool for the job
• Familiarity with agent tracing and monitoring concepts — you understand how to observe agent behavior in production (e.g., trace logs, token usage, latency, failure modes) and know why it matters for reliability and governance
• API and integration experience — you can read API docs, build connectors, and wire systems together without hand-holding
• Strong communication skills — you can explain technical architecture to non-technical stakeholders and write documentation people actually use
• Collaborative and cross-functional — comfortable working across IT, data, and business teams in a fast-moving environment

Desirable

• Ping Identity is seeking a technically strong, hands-on AI Automation Engineer to join the Data & AI team within the Office of the CIO. This is a builder role — you won't just configure platforms, you'll write code, design custom agent architectures, and deliver production-grade AI automation that drives measurable business impact across the enterprise
• Where the first hire focuses on business process design and low-code/no-code delivery, you will own the programmatic side of our agent stack — building custom agents in Databricks, developing integrations and connectors, and tackling automation challenges that require real engineering depth
• As our second AI Automation Engineer, you'll work alongside our existing AI Automation Engineer and report to the Director of AI & Data.
• This role is for someone who is equally comfortable in a Jupyter notebook and a stakeholder meeting — someone who can translate a complex business problem into a working, governed solution
• Build and deploy custom AI agents using Python and Databricks, including complex, multi-step agents that go beyond what low-code platforms can handle
• Design and implement integrations between Glean, Databricks, and enterprise systems (Salesforce, Jira, Freshservice, etc.) — including custom connectors and data pipelines that feed our AI stack
• Own the technical delivery of high-code agent use cases in our AI operating model — from requirements through deployment, monitoring, and iteration
• Collaborate with the AI Automation Engineer (business-focused) to divide and conquer the agent backlog: they own the canvas/low-code layer, you own the programmatic layer
• Partner with data engineers to ensure agents have access to clean, reliable, well-governed data via Databricks Unity Catalog and our lakehouse architecture
• Support the agent governance process — writing technical specs, participating in governance reviews, and ensuring custom agents meet security, PII, and compliance requirements before production deployment
• Contribute to the agent framework and tooling used by the broader AI team — prompt templates, evaluation harnesses, reusable agent components, and CI/CD pipelines for agent deployment
• Triage and resolve technical escalations from the AI platform support queue that require engineering-level investigation