AI Analytics Engineer for Software Delivery

Opplane; United States; On-site; USD 120,000 - 190,000; Full time

Job summary

Opplane seeks a senior data analytics professional to design experiments across the software delivery lifecycle. You will build classification and evaluation pipelines, and translate findings into actionable capabilities for delivery teams.

You will partner with a data engineer handling extraction and metrics, while you interpret results to drive practical improvements and governance around AI-powered processes.

Qualifications

• 7+ years spanning software engineering and quantitative analysis.
• Production experience with LLM applications and evaluation.
• Strong Python and SQL skills with a statistical stack.
• Experience in experimental design and causal inference methodologies.
• Ability to aggregate, join, and model complex data sources.
• Familiarity with the software delivery lifecycle and CI/CD processes.
• Excellent communication to non-technical stakeholders.

Responsibilities

• Design and run experiments to establish what helps, across teams and tools.
• Own the analytical layer of the measurement program and longitudinal analyses.
• Build LLM-as-judge and classification pipelines with robust evaluation.
• Extend AI capabilities to test authoring, environment setup, and migration.
• Collaborate with constrained teams to identify bottlenecks and target impact.
• Develop evaluation frameworks and telemetry for model performance and cost.

Skills

Python

SQL

Experimental design

Causal inference

Data visualization

LLM familiarity

Communication

Education

Bachelor's degree in CS/Engineering

Master's degree preferred

Tools

Pandas

scikit-learn

PostgreSQL

Job description

Opplane seeks a senior data analytics professional to design experiments across the software delivery lifecycle. You will build classification and evaluation pipelines, and translate findings into actionable capabilities for delivery teams.

You will partner with a data engineer handling extraction and metrics, while you interpret results to drive practical improvements and governance around AI-powered processes.