Principal Software Engineer

Job Title:

Principal Software Engineer — AI Tooling & Quality Engineering Platforms

Team:
Global Quality Engineering (GQE)

Location:

New York City, Los Angeles, San Francisco

Paramount Skydance Corp. is seeking a Principal Software Engineer to architect, build, and scale AI‑driven tooling and engineering platforms that accelerate quality, automation, and developer productivity across the Global Quality Engineering organization. This senior technical leadership role focuses on designing intelligent systems, internal developer tools, and automation accelerators to elevate testing maturity and modernize how GQE delivers quality at scale.

• Design and build AI‑powered tools that accelerate test creation, code generation, defect triage, log/telemetry summarization, and root‑cause analysis.
• Architect retrieval‑augmented generation (RAG) systems, embeddings pipelines, and domain‑specific LLM integrations tailored to GQE workflows.
• Develop internal developer tools, CLIs, services, and micro‑platforms that integrate seamlessly with existing automation frameworks and CI/CD systems.
• Build scalable APIs and services that expose AI capabilities to GQE teams across brands and platforms.
• Create systems that automatically evaluate test failures, classify flakiness, detect patterns, and recommend fixes.
• Build AI‑assisted test authoring tools that generate high‑quality test scaffolds, assertions, mocks, and data models.
• Integrate AI‑driven insights into CI/CD pipelines to reduce triage time, improve signal quality, and accelerate release readiness.
• Partner with automation framework owners to embed AI capabilities into existing Java‑based frameworks.
• Serve as a senior technical leader guiding the vision for AI‑enabled quality engineering across the organization.
• Mentor engineers on AI tooling, platform engineering, and modern software development practices.
• Evaluate emerging AI technologies and frameworks to identify high‑impact opportunities.
• Establish engineering best practices for reliability, observability, performance, and maintainability of AI systems.
• Collaborate with Platform Engineering, Data Engineering, Playback/Video Engineering, and Product teams to integrate AI tooling into core workflows.
• Work with QE leadership to understand pain points, define requirements, and prioritize high‑value AI capabilities.
• Collaborate with Dev Ops and CI/CD teams to ensure AI tools operate reliably in production pipelines.
• Build high‑performance backend services using Java, Kotlin, Python, or Node.js.
• Implement vector databases, embeddings pipelines, and retrieval systems using Pinecone, Weaviate, FAISS, or Open Search.
• Develop microservices, event‑driven systems, and distributed architectures deployed on Kubernetes and cloud platforms.
• Integrate with Git Hub, Git Hub Actions, Jenkins, and internal automation frameworks to deliver seamless developer experiences.

• 8+ years of software engineering experience with robust backend development expertise (Java, Kotlin, Python, or similar).
• Proven experience architecting and building production‑grade AI or ML‑powered systems, including LLM integrations, RAG pipelines, or intelligent automation tools.
• Strong knowledge of distributed systems, microservices, cloud platforms (AWS, GCP, OCI, or Azure), and containerization (Docker, Kubernetes).
• Hands‑on experience with vector databases, embeddings, prompt engineering, and LLM orchestration frameworks.
• Experience integrating tools into CI/CD pipelines and developer workflows.
• Strong architectural judgment, systems thinking, and ability to design scalable internal platforms.
• Excellent communication skills and ability to influence across engineering, product, and quality organizations.

• Experience building internal developer platforms, productivity tools, or automation accelerators.
• Familiarity with test automation frameworks, quality engineering workflows, or large‑scale testing systems.
• Experience with observability stacks (Grafana, Prometheus, Data Dog, New Relic) and telemetry pipelines.
• Background in media/streaming, distributed playback systems, or device‑level testing environments.