AI and DevOps Engineer – Human Capital (759BDB3)

Referment; New York (NY); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Referment, in partnership with a global investment and technology firm, seeks an AI/DevOps operations professional for its People Analytics and AI group in New York. You will own reliability of data infrastructure, ETL pipelines, and analytics tooling used across technical and non-technical users.

You will build, deploy, and maintain dashboards and reporting products in Python, manage release/testing, and ensure robust monitoring and incident response.

Qualifications

• Bachelor's degree in a technical field.
• 3+ years of relevant experience in DevOps, data engineering, or AI operations.
• Strong Python skills for data transformation and analysis; experience with Git and CI/CD pipelines.

Responsibilities

• Build, deploy, and maintain automated reporting products and dashboards, and the ETL pipelines behind them.
• Manage release and testing for analytics tooling and data pipelines; onboard new developers.
• Ensure reliability of shared infrastructure with monitoring, alerting, usage tracking, and incident response.
• Administer prompt versioning and change control for AI systems, including issue investigations.
• Run AI model evaluations, migrations, and manage query logging with privacy safeguards.
• Maintain the team’s Claude skills and plugins for both technical and non-technical users.

Skills

Python

Git

CI/CD

AI operations

Data wrangling

Analytical thinking

Education

Bachelor's degree in a technical field

Job description

Referment is working with a global investment and technology firm on an AI and DevOps engineer opening inside its People Analytics and AI group. The team designs, builds, and operates generative AI tools that shape how the firm attracts, assesses, develops, and retains talent, serving both technical and non-technical users across the business. This hire is the operational backbone of that team: keeping shared data and AI infrastructure healthy and reliable, from back-end pipelines to developer tooling and user-facing reporting.

You’ll work at the intersection of people strategy, product design, and emerging AI capabilities, partnering with technical leads and supporting non-technical users on tools that depend on getting the plumbing right.

The Role

• Build, deploy, and maintain the team's automated reporting products and dashboards, primarily in Python, along with the ETL pipelines and people-analytics data infrastructure behind them.
• Manage release and testing for people-analytics tooling and data pipelines, keep repositories healthy (CI checks, dependency updates), and onboard new developers.
• Ensure reliability of shared infrastructure and applications by setting up monitoring and alerting, tracking usage, and leading incident response.
• Administer prompt versioning and change control for AI systems, including first-line investigation of feature issues.
• Run AI model evaluations, carry out migrations as models are updated or retired, and manage query logging and usage analytics with appropriate privacy safeguards.
• Maintain the team's Claude skills and plugins for both technical and non-technical users.

What We're Looking For

• A bachelor's degree in a technical field and at least three years of relevant experience in DevOps, data engineering, or AI operations.
• Fluency in Python, including for data transformation and analysis, plus hands‑on experience with Git and CI/CD pipelines.
• Demonstrated experience managing deployment pipelines, environment setup, and developer onboarding, along with prompt management and troubleshooting in AI or machine learning environments.
• An affinity for data wrangling and for finding the right way to visualize data for a senior audience; people-analytics experience is a plus.
• Proven ability to operate in ambiguous, fast-moving environments, see how small details affect the bigger picture, and drive work across cross-functional dependencies.
• Excellent organizational and communication skills, with the discretion to handle confidential information appropriately.

This could suit a DevOps or data engineer who has moved into AI platform work and wants to own the reliability of systems that real people decisions depend on, in close partnership with both engineers and business users.

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