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.

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 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.

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.

About the Opportunity

We are seeking a Senior Software Engineer to design, build, deploy, monitor, and optimize production-ready ML services in regulated healthcare. You will work hands-on to package, test, orchestrate, deploy, and maintain ML models, improve workflows, and implement CI/CD and automated testing to ensure reliability, performance, and faster delivery of business value.

Responsibilities

• Design, build, and deploy scalable AI/ML services with clear service boundaries, making pragmatic trade-offs across performance, maintainability, security, and long-term extensibility.

• Own full lifecycle delivery of complex features from architectural planning and API design to implementation and post-release observability.

• Define and enforce robust verification and validation for AI services including unit, integration, and end-to-end testing to ensure reliability of code in production and compliance with our Quality Management System and regulatory standards. 

• Integrate CI/CD and MLOps practices for automated model builds, testing, and deployment.

• Translate product requirements into well-defined technical designs proactively shaping implementation details.

• Contribute to the necessary technical documentation required for regulatory submissions.

• Collaborate cross functionally to reduce knowledge silos and ensure continuity in critical systems through cross-training and documentation. 

Requirements

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field

• 7+ years of software engineering experience, with expertise in AI production systems (Python, PyTorch) and data services (SQL, Postgres, NoSQL, Redis or similar).

• Experience with unit, integration testing for ML models, including data validation, correctness checks, and reproducibility.

• Experience with CI/CD, Orchestration tools (Airflow, MLflow, Kubernetes, Terraform) and ML/data platforms(SageMaker, Databricks, Unity Catalog, Snowflake/Snowpark) to build scalable ML data pipelines and model workflows.

• Strong collaboration skills to work effectively with ML scientists, engineers, and regulatory teams.

• Comfortable operating in ambiguity and driving clarity across product, engineering, and business stakeholders.

This role is eligible for a 15% target annual bonus, resulting in the following base salary and Total Target Compensation (TTC) ranges:

• Base Salary: $153,000 - $179,000
• TTC: $176,000 - $206,000

*Total Target Compensation (TTC): Total Cash Compensation (including base pay, variable pay, commission, bonuses, etc.) Additionally, stock options, paid benefits, and employee perks are part of your total rewards.

About the Opportunity

We are seeking a senior machine learning software engineer to design, build, deploy, monitor, and optimize production-ready ML services in regulated healthcare. You will work hands-on to package, test, orchestrate, deploy, and maintain ML models, improve workflows, and implement CI/CD and automated testing to ensure reliability, performance, and faster delivery of business value.

Responsibilities

• Collaborate with AI scientists to package and deploy ML models, ensuring reproducibility, versioning, and compliance.
• Build and maintain model serving infrastructure including monitoring, drift detection, automated retraining, and logging.
• Implement unit, integration, and system-level testing for ML models, covering data validation, model correctness, and deployment workflows.
• Develop and operate end-to-end ML pipelines: ingestion → preprocessing → feature engineering → evaluation → deployment → monitoring.
• Integrate CI/CD and MLOps practices for automated model builds, testing, and deployment.
• Identify and resolve workflow inefficiencies or gaps between research and production.
• Recommend and integrate frameworks, libraries, and infrastructure to improve pipeline efficiency, maintainability, and observability.
• Collaborate cross-functionally to ensure compliance with regulatory requirements (FDA/HIPAA) in production ML workflows.
  
  

Requirements

• 7+ years of experience in software engineering for ML production or ML platform delivery.
• Hands-on experience deploying ML models via APIs, batch pipelines, or streaming inference.
• Proficiency in Python (required), Java, or similar, with software engineering best practices for ML workflows.
• Experience with unit, integration, and pipeline-level testing for ML models, including data validation, correctness checks, and reproducibility.
• Familiarity with cloud platforms (AWS preferred: SageMaker, S3, EC2) and reproducible ML pipelines.
• Experience with CI/CD, Orchestration tools (Airflow, MLflow, Kubernetes, Terraform) and ML/data platforms(SageMaker, Databricks, Unity Catalog, Snowflake/Snowpark) to build scalable ML data pipelines and model workflows.
• Strong collaboration skills to work effectively with AI scientists, software engineers, and regulatory teams.

The base salary range for this role varies by location and is aligned to market benchmarks.

• Candidates located in higher-cost labor markets, including California, Washington, New York, New Jersey, Connecticut, Massachusetts, and Washington, DC represent the middle to high end of the range, while candidates located in all other U.S. locations represent the low to middle end of the range.
• Final compensation is determined based on location, experience, skills, and internal equity.

This role is eligible for a 15% target annual bonus, resulting in the following base salary and Total Target Compensation (TTC) ranges:

• Base Salary: $153,000 - $179,000
• TTC: $176,000 - $206,000

*Total Target Compensation (TTC): Total Cash Compensation (including base pay, variable pay, commission, bonuses, etc.) Additionally, stock options, paid benefits, and employee perks are part of your total rewards.