Software Engineer II

Inspyr Solutions
San Francisco, United States of America
5 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior
Compensation
$ 233K

Job location

San Francisco, United States of America

Tech stack

Artificial Intelligence
Amazon Web Services (AWS)
Computer Programming
Information Engineering
ETL
Data Security
Data Systems
Data Visualization
Software Debugging
Monitoring of Systems
Python
Software Engineering
Tableau
TypeScript
Data Processing
Google Cloud Platform
Cloud Platform System
Large Language Models
Grafana
Generative AI
Backend
AI Platforms
Data Analytics
Tools for Reporting
Api Design
Looker Analytics
GPT
Data Pipelines

Job description

The Production AI team is the core AI enablement function, focused on accelerating the adoption of AI tools and technologies across the company. We partner with teams across the company to evaluate, integrate, and support AI platforms-from LLM tooling to agentic systems-while building scalable solutions that empower teams to move faster and create better player experiences. As a Software Engineer II on the Production AI team, you will focus on evolving and scaling our AI metrics and analytics ecosystem. You''ll take ownership of an existing metrics pipeline-stabilizing, extending, and operationalizing it-while partnering closely with internal stakeholders and external vendors to ensure data quality, reliability, and usability. This role sits at the intersection of data engineering, platform engineering, and AI enablement. You''ll work directly with teams across AI Foundations to define meaningful metrics, deliver actionable insights, and improve how we measure the impact of AI systems. Over time, you''ll expand into broader Production AI initiatives as you build domain expertise.

Responsibilities

  • Own and evolve Production AI''s metrics pipeline, improving reliability, scalability, and data quality
  • Diagnose and resolve pipeline issues, coordinating with external tool vendors and internal partners to drive fixes and improvements
  • Extend existing data pipelines to support new use cases, teams, and AI products
  • Design and implement analytics and visualization solutions (dashboards, reporting tools, data access layers) for stakeholders
  • Partner with product, engineering, and AI teams to define meaningful metrics and iterate on measurement strategies
  • Ensure data is accessible, trustworthy, and actionable for technical and non-technical consumers
  • Contribute to operational excellence through monitoring, alerting, and documentation of data systems
  • Support additional AI Foundations teams with similar metrics and analytics needs as required
  • Contribute to broader Production AI engineering efforts over time as domain knowledge grows, * Designing and scaling metrics systems for AI platforms at enterprise scale
  • Applying AI/LLM systems in production environments and measuring their impact
  • Partnering across a wide range of teams to shape how AI success is defined and tracked
  • Expanding into broader AI platform and application development within Production AI

Requirements

  • 3-5 years of professional software engineering experience or equivalent
  • Strong programming skills in Python, Typescript, or similar languages
  • Experience building, maintaining, or operating data pipelines or backend services
  • Experience working with APIs, data processing systems, and debugging production issues
  • Familiarity with cloud environments (e.g., AWS, Google Cloud Platform) and modern data tooling
  • Ability to independently own and deliver on moderately scoped projects
  • Strong communication skills and ability to collaborate with cross-functional stakeholders, * Experience with analytics, metrics systems, or data visualization tools (e.g., Tableau, Looker, Grafana)
  • Familiarity with generative AI tools (e.g., ChatGPT, Claude, Gemini) and agentic workflows
  • Experience working with third-party vendors or external platforms
  • Knowledge of data modeling, ETL/ELT pipelines, and data quality best practices
  • Experience building internal tools or platforms for developer or product teams
  • Familiarity with observability and monitoring systems

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