Senior Applied AI Engineer

Paramount Pictures; Burbank (CA); On-site; USD 139,000 - 180,000; Full time

Benefits offered by this job

Attractive compensation

Generous paid time off

Comprehensive benefits packages

Opportunities for virtual engagement events

Job summary

Paramount Pictures seeks a Senior Applied AI Engineer in Burbank, California, to architect and operationalize AI-driven solutions that enhance software quality across the enterprise. The role requires over 7 years of experience in machine learning and strong expertise in Java, Python, and Google Cloud Vertex AI. You will work with cross-functional teams to improve automation efficiency and deliver predictive quality insights while mentoring engineers and leading architectural decisions on AI systems.

Qualifications

• 7+ years of experience in machine learning engineering or applied AI.
• Strong expertise in Java, Python, and modern LLM tooling.
• Experience with Google Cloud Vertex AI for production deployment.
• Solid understanding of quality engineering tools and CI/CD systems.

Responsibilities

• Architect and deploy AI/ML systems for software quality enhancements.
• Optimize pipelines for AI-driven testing strategies.
• Mentor junior engineers on applied machine learning practices.
• Collaborate cross-functionally with GQE, DevOps, and SRE teams.

Skills

Machine learning engineering

Software engineering

Applied AI

Java

Python

PyTorch/TensorFlow

Google Cloud Vertex AI

Automation frameworks

MLOps platforms

Tools

Selenium

Appium

Playwright

pytest

JUnit

TestNG

Job description

Job Title

Senior Applied AI Engineer

Team

Global Quality Engineering

Overview

Paramount Skydance Corp. is seeking a Senior Applied AI Engineer to architect, build, and operationalize AI-driven solutions that transform how we deliver software quality across the enterprise. This role blends advanced machine learning, large language models, and software engineering expertise to improve automation efficiency, accelerate feedback loops, enhance defect detection, and deliver predictive quality insights.

You will be a key member of the Global Quality Engineering (GQE) team and partner with DevOps, SRE, and Infosec teams to embed AI capabilities directly into the SDLC, leveraging modern platforms such as Vertex AI to deliver scalable, resilient, and impactful AI solutions for Quality Engineering initiatives.

Key Responsibilities

AI/ML Solution Development

• Architect, develop, and deploy end-to-end AI/ML systems addressing key QE workflows (e.g., bug prediction, app confidence scoring for incremental releases, flaky test detection, intelligent test prioritization, anomaly detection).
• Build, optimize, and tune RAG pipelines, including embedding and vector store selection, chunking and retrieval optimization, hallucination mitigation and grounding techniques, and hybrid LLM architectures.
• Perform LLM fine-tuning (full-model, LoRA/QLoRA, instruction tuning) and determine when fine-tuning is appropriate versus RAG-only or hybrid approaches.
• Build LLM tools for test case generation (manual and automated), synthetic test data creation, log and telemetry summarization, and automated triage and quality insights.
• Develop model evaluation frameworks ensuring accuracy, robustness, and safe behavior over time.

Global Quality Engineering Innovation

• Identify and prioritize opportunities to integrate AI automation across test strategy, execution, triage, and release decisioning.
• Integrate AI into CI/CD pipelines for dynamic risk-based testing, anomaly detection, and intelligent quality gates.
• Build solutions that analyze logs, traces, telemetry, and user signals to detect emerging quality risks.
• Leverage Google Cloud Vertex AI to build scalable, production-grade AI systems, including Vertex AI Training, Tuning (LoRA/QLoRA), and Custom Jobs; Vertex AI Vector Search for high-performance retrieval; Vertex AI Pipelines for automated ML workflows; Vertex AI Online Endpoints for real-time inference.
• Integrate Vertex AI with GCP services (BigQuery, Cloud Run, GKE, Pub/Sub) for full production deployment.

Technical Leadership

• Lead architectural decisions on LLM system design, MLOps, data pipelines, and monitoring strategies.
• Mentor engineers on applied ML, modern AI development, prompt engineering, and RAG-vs-fine-tuning tradeoffs.
• Partner in the creation of engineering standards for model governance, safety, code quality, and scalable AI development.

Cross-Functional Collaboration

• Collaborate with peers in GQE as well as DevOps, SRE and Infosec teams to translate quality challenges into high-value AI solutions that accelerate testing.
• Work closely with Data Engineering to ensure training data quality, governance, privacy, and compliance.
• Clearly communicate complex concepts to a variety of audiences including executives, engineers, and non-technical stakeholders.

Required Qualifications

• 7+ years of experience in machine learning engineering, software engineering, or applied AI.
• Strong expertise in Java, Python, PyTorch/TensorFlow, and modern LLM tooling.
• Deep hands-on experience with RAG systems, including vector database design and embedding evaluation, retrieval optimization and hybrid architectures, hallucination reduction and grounding strategies.
• Strong hands-on experience with LLM fine-tuning, including full-model and parameter-efficient approaches, cost, latency, and behavior tradeoff analysis.
• Expertise selecting between RAG vs. fine-tuning vs. hybrid approaches based on data characteristics, quality needs, and business constraints.
• Production experience with Google Cloud Vertex AI, including training, tuning, pipelines, Vector Search, and real-time model deployment.
• Solid understanding of quality engineering tools, automation frameworks (Selenium, Appium, Playwright, pytest, JUnit, TestNG), and CI/CD systems.
• Experience with MLOps platforms (MLflow, Kubeflow, SageMaker, Databricks) and cloud platforms (Azure, AWS, or GCP).

Preferred Qualifications

• Experience building AI systems specifically for engineering productivity or quality engineering.
• Familiarity with observability tools (Grafana, Prometheus, OpenTelemetry), code analysis and static/dynamic analysis tools.
• Experience mentoring engineers or serving as a technical lead on cross-functional AI projects.
• Architectural judgment – mastery of when to apply RAG, fine-tuning, hybrid retrieval models, or classical ML.
• Innovation mindset – constantly identifying opportunities to improve velocity, quality, and automation with AI.
• Systems thinking – ability to model complex SDLC and QE workflows.
• Strong communication and the ability to drive consensus among stakeholders.
• Ownership – ability to drive initiatives end-to-end with autonomy.

Success Metrics

• Reduction in escaped defects and increased early bug detection.
• Significant reduction in flaky tests and triage time.
• Faster release cycles through intelligent, AI-driven testing strategies.
• Improved developer and tester productivity via AI-powered tooling.
• Successful deployment and adoption of enterprise-grade AI systems across the org.

What We Offer

• Attractive compensation and comprehensive benefits packages. Check out our full list of benefits here: https://www.paramount.com/careers/benefits
• Generous paid time off.
• An exciting and fulfilling opportunity to be part of one of Paramount’s most dynamic teams.
• Opportunities for both on-site and virtual engagement events.
• Unique opportunities to make meaningful connections and build a vibrant community, both inside and outside the workplace.
• Explore life at Paramount: https://www.paramount.com/careers/life-at-paramount

Hiring Salary Range: $139,000.00 - $180,000.00.

Payroll benefits include medical, dental, vision, 401(k) plan, life insurance coverage, disability benefits, tuition assistance program, and PTO, as applicable.

Paramount is an equal opportunity employer (EOE) including disability and veteran status.