Principal Software Engineer

This is a Full Time Job

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Weâ??ve got the brands, weâ??ve got the stars, weâ??ve got the power to achieve our mission to entertain the planet ? now all weâ??re missing isâ?¦ YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter ? both for our audiences and our employees ? and aim to leave a positive mark on culture.

Job Title: Principal Software Engineer - AI Tooling & Quality Engineering Platforms
Team: Global Quality Engineering (GQE)
Location: New York City, Los Angeles, San Francisco

OverviewParamount Skydance Corp. is seeking a Principal Software Engineer to architect, build,
and scale AIâ?'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 AIâ?'powered
solutions that streamline test creation, triage, analysis, and execution. This role requires
deep software engineering expertise, deep architectural judgment, and handsâ?'on
experience applying LLMs, retrieval systems, and automation frameworks to real
engineering workflows.

This is not a traditional â??in testâ? role. You will be building AI platforms, internal tools,
and automation accelerators that enable hundreds of engineers across GQE.

Key Responsibilities

AI Tooling Architecture & Platform Development
â- Design and build AIâ?'powered tools that accelerate test creation, code generation,defect triage, log/telemetry summarization, and rootâ?'cause analysis.
â- Architect retrievalâ?'augmented generation (RAG) systems, embeddings pipelines,
and domainâ?'specific LLM integrations tailored to GQE workflows.
â- Develop internal developer tools, CLIs, services, and microâ?'platforms that
integrate seamlessly with existing automation frameworks and CI/CD systems.
â- Build scalable APIs and services that expose AI capabilities to GQE teams
across brands and platforms.

Intelligent Automation & Quality Acceleration

â- Create systems that automatically evaluate test failures, classify flakiness, detect
patterns, and recommend fixes.
â- Build AIâ?'assisted test authoring tools that generate highâ?'quality test scaffolds,
assertions, mocks, and data models.
â- Integrate AIâ?'driven insights into CI/CD pipelines to reduce triage time, improve
signal quality, and accelerate release readiness.
â- Partner with automation framework owners to embed AI capabilities into existing
Javaâ?'based frameworks.

Engineering Excellence & Technical Leadership

â- Serve as a senior technical leader leading the vision for AIâ?'enabled quality
engineering across the organization.
â- Mentor engineers across GQE on AI tooling, platform engineering, and modern
software development practices.
â- Evaluate emerging AI technologies, frameworks, and platforms to identify
practical, highâ?'impact opportunities.
â- Establish engineering best practices for reliability, observability, performance, and
maintainability of AI systems.

Crossâ?'Functional Collaboration

â- Partner with Platform Engineering, Data Engineering, Playback/Video
Engineering, and Product teams to integrate AI tooling into core workflows.
â- Work with QE leadership to understand pain points, define requirements, and
prioritize highâ?'value AI capabilities.
â- Collaborate with DevOps and CI/CD teams to ensure AI tools operate dependably in
production pipelines.

Technical Execution

â- Build highâ?'performance backend services using Java, Kotlin, Python, or Node.js.
â- Implement vector databases, embeddings pipelines, and retrieval systems using
tools such as Pinecone, Weaviate, FAISS, or OpenSearch.
â- Develop microservices, eventâ?'driven systems, and distributed architectures
deployed on Kubernetes and cloud platforms.
â- Integrate with GitHub, GitHub Actions, Jenkins, and internal automation
frameworks to deliver seamless developer experiences.

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

Preferred Qualifications
â- Experience building internal developer platforms, productivity tools, or
automation accelerators.
â- Familiarity with test automation frameworks, quality engineering workflows, or
largeâ?'scale testing systems.
â- Experience with observability stacks (Grafana, Prometheus, DataDog, NewRelic)
and telemetry pipelines.
â- Background in media/streaming, distributed playback systems, or deviceâ?'level
testing environments.

Key Competencies

â- Technical Leadership ? sets architectural direction and elevates engineering
maturity.
â- AI Fluency ? practical judgment on where AI delivers measurable value.
â- Systems Thinking ? ability to design platforms that scale across teams and
brands.
â- Innovation ? identifies opportunities to transform workflows through intelligent
automation.
â- Ownership ? drives complex initiatives endâ?'toâ?'end with autonomy and clarity.

Success Metrics

â- Delivery of AI tooling that reduces triage time, accelerates test creation, and
improves signal quality.
â- Adoption of AIâ?'powered tools across GQE teams and measurable improvements
in productivity.
â- Reduction in flakiness, false positives, and manual triage effort through intelligent
automation.
â- Increased engineering efficiency and faster release cycles enabled by
AIâ?'augmented workflows.
â- Strong crossâ?'functional alignment and consistent usage of AI platforms across brands and pillars.

#LI-JC1

ADDITIONAL INFORMA

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