AI Automation Engineer

Ping Identity•Denver, CO

About The Position

At Ping Identity, we believe in making digital experiences both secure and seamless for all users, without compromise. We call this digital freedom. And it's not just something we provide our customers. It's something that inspires our company. People don't come here to join a culture that's built on digital freedom. They come to cultivate it. Our intelligent, cloud identity platform lets people shop, work, bank, and interact wherever and however they want. Without friction. Without fear. While protecting digital identities is at the core of our technology, protecting individual identities is at the core of our culture. We champion every identity. One of our core values, Respect Individuality, reminds us to celebrate differences so you are empowered to bring your authentic self to work. We're headquartered in Denver, Colorado and we have offices and employees around the globe. We serve the largest, most demanding enterprises worldwide, including more than half of the Fortune 100. At Ping Identity, we're changing the way people and businesses think about cybersecurity, digital experiences, and identity and access management. The Role 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. As our second AI Automation Engineer, you'll work alongside our existing AI Automation Engineer and report to the Director of AI & Data. 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. 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.

Requirements

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.

Nice To Haves

You've played a hands-on technical role in top-down, enterprise AI transformation initiatives — where the work was tied to clear business outcomes, P&L targets, or measurable productivity gains, not just engineering deliverables.
You have hands-on experience with Glean — building agents, configuring connectors, or working with the Glean API.
You've built production-grade agentic workflows incorporating RAG, tool use, orchestration layers (LangGraph, CrewAI, custom), or multi-agent patterns.
You have experience with Salesforce integrations or have built agents that take actions inside enterprise SaaS platforms.
You're familiar with MLOps or LLMOps practices — model evaluation, prompt versioning, observability, and deployment pipelines.
You have a background in data engineering and are comfortable with SQL, Delta tables, and Unity Catalog governance patterns.
You've contributed to AI governance or responsible AI programs — specifically around agent safety, PII handling, and access control.
You've worked in a B2B SaaS or enterprise software environment and understand how to build solutions that are supportable at scale.

Responsibilities

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.