AI Automation Engineer

Pingidentity; Denver (CO); On-site; USD 130,766 - 150,000; Full time

Benefits offered by this job

Generous PTO & Holiday Schedule

Parental Leave

Progressive Healthcare Options

Retirement Programs

Education Reimbursement

Commuter Offset

Job summary

Pingidentity is looking for a hands-on AI Automation Engineer to join its Data & AI team in Denver, Colorado. This role focuses on building custom AI agents using Python and Databricks. The ideal candidate will collaborate with existing engineers, develop integrations, and ensure the proper governance of AI agents.

You should have 3-5 years of software engineering experience and be comfortable in both technical and stakeholder settings. The position offers a competitive salary and comprehensive benefits.

Qualifications

• 3–5 years of hands-on software engineering or data engineering experience.
• Experience building on Databricks and designing custom agents.
• Proficiency with agentic coding frameworks.

Responsibilities

• Build and deploy custom AI agents using Python.
• Design and implement integrations between various systems.
• Own the technical delivery of high-code agent use cases.

Skills

Python

Databricks

Building production systems

API and integration

Strong communication

Tools

Databricks platforms

Glean

Job description

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 that can translate a complex business problem into a working, governed solution.

What You’ll Do

• 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 specifications, 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.

What You’ll Bring

• 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.

You Have an Advantage If

• 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.

Base Salary Range: $130,766 - $150,000

In accordance with Colorado's Equal Pay for Equal Work Act (SB 19‑085), the approximate compensation range for this role in Colorado is listed above. Final compensation will be determined by various factors, such as knowledge, skills, and abilities.

Our Benefits

• Generous PTO & Holiday Schedule
• Parental Leave
• Progressive Healthcare Options
• Retirement Programs
• Opportunity for Education Reimbursement
• Commuter Offset (Specific locations)

We are an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex including sexual orientation and gender identity, national origin, disability, protected Veteran Status, or any other characteristic protected by applicable federal, state, or local law.