Principal Software Engineer

Paramount•Burbank, CA

3d•Onsite

About The Position

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 is a senior technical leadership role focused on designing intelligent systems, internal developer tools, and automation accelerators that elevate testing maturity and modernize how GQE delivers quality at scale. You will partner closely with engineering, platform, and data teams to build AI-powered solutions that streamline test creation, triage, analysis, and execution. This role requires deep software engineering expertise, deep architectural judgment, and hands-on experience applying LLMs, retrieval systems, and automation frameworks to real engineering workflows. This is not a traditional “in test” role. You will be building AI platforms, internal tools, and automation accelerators that enable hundreds of engineers across GQE.

Requirements

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.

Nice To Haves

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, DataDog, NewRelic) and telemetry pipelines.
Background in media/streaming, distributed playback systems, or device-level testing environments.

Responsibilities

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 leading the vision for AI-enabled quality engineering across the organization.
Mentor engineers across GQE on AI tooling, platform engineering, and modern software development practices.
Evaluate emerging AI technologies, frameworks, and platforms to identify practical, high-impact opportunities.
Establish engineering best practices for reliability, observability, performance, and maintainability of AI systems.
Partner 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 DevOps and CI/CD teams to ensure AI tools operate dependably in production pipelines.
Build high-performance backend services using Java, Kotlin, Python, or Node.js.
Implement vector databases, embeddings pipelines, and retrieval systems using tools such as Pinecone, Weaviate, FAISS, or OpenSearch.
Develop microservices, event-driven systems, and distributed architectures deployed on Kubernetes and cloud platforms.
Integrate with GitHub, GitHub Actions, Jenkins, and internal automation frameworks to deliver seamless developer experiences.