Principal Software Engineer

Paramount

Principal Software Engineer developing AI-driven tooling and engineering platforms for Paramount's Global Quality Engineering team. Leading design and implementation of intelligent systems to enhance developer productivity and automation.

Posted 5/2/2026full-timeNew York City • California, New York • 🇺🇸 United StatesLead💰 $203,000 - $260,000 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsDockerGoogle Cloud PlatformJavaJavaScriptJenkinsKotlinKubernetesMicroservicesNode.jsPython

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

medical
dental
vision
401(k) plan
life insurance coverage
disability benefits
tuition assistance program
PTO
bonus eligible

ATS Keywords

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Hard Skills & Tools

JavaKotlinPythonNode.jsAI systemsML systemsmicroservicesdistributed systemsvector databasesembeddings

Soft Skills

communicationinfluencementoringarchitectural judgmentsystems thinking