Lead Software Engineer

Lead Software Engineer

Paramount Skydance Corp. is seeking a Lead Software Engineer to architect, lead, and deliver enterprise-scale AI tooling and internal platforms that transform how Global Quality Engineering operates. This is not an "in test" role — you will be building foundational AI platforms, internal developer tools, and intelligent automation systems used across multiple brands and engineering pillars. You will define technical direction, lead complex initiatives, and build systems that accelerate automation, triage, and analysis at scale.

Key Responsibilities:

• Architect large-scale AI platforms, RAG systems, and intelligent automation services that support GQE.
• Lead the design of internal developer tools, micro-platforms, and APIs that integrate AI into core engineering workflows.
• Build systems that automatically evaluate failures, classify flakiness, detect patterns, and recommend fixes.
• Partner with the QE executive team to define long-term AI strategy and roadmap, ensuring alignment with Platform Engineering, Data Engineering, Playback/Video Engineering, and Product teams.
• Mentor engineers across GQE and elevate engineering maturity through best practices and architectural guidance.
• Evaluate and integrate emerging AI technologies that deliver measurable productivity and quality improvements.

Qualifications:

• 6+ years of software engineering experience with deep backend and architectural expertise.
• Proven experience building and scaling production AI systems, including LLM integrations and RAG pipelines.
• Solid systems thinking and ability to design platforms used across multiple teams and brands.
• Expertise in cloud platforms, distributed systems, and container orchestration.
• Demonstrated management in technical strategy, platform design, and cross-functional influence.
• Great communication skills and ability to drive alignment across engineering organizations.

Key Competencies:

• Organization-level technical leadership.
• Ability to define platform strategy and long-term architecture.
• Deep AI fluency and realistic judgment on where AI adds value.
• Strong mentorship and influence across teams.

Success Metrics:

• Enterprise-wide adoption of AI tooling with measurable improvements in productivity and quality.
• Significant reduction in triage time, flakiness, and manual analysis through intelligent automation.
• Clear architectural direction and roadmap for AI-enabled quality engineering.
• Strong uplift in engineering excellence across GQE.