Title: Lead Software Engineer — Studio Digital Supply Chain
Location: Burbank, CA 91505
Duration: 12 Months Contract with potential for full-time conversion
Schedule: Hybrid, 4 days onsite per week
Ideal Candidate Profile
- The ideal candidate is a hands-on technical leader who can balance architecture, implementation, and stakeholder communication. This person should be comfortable driving technical direction, mentoring and influencing engineers, explaining complex designs in business terms, and building reliable systems that support mission-critical media and content workflows.
Interview Process:
1. Initial hiring manager screen.
2. Technical interview focused on code review, debugging, and production support scenarios.
3. System design interview.
4. Final interview with engineering leadership.
Job Description:
We are in search of a Staff (Lead) Software Engineer to help build the next generation of intelligent infrastructure for the Studio Digital Supply Chain. This role blends expertise in backend software engineering, workflow and AI to design and deliver agentic platforms that support workflows like mastering, localization, and delivery.
This is a hands-on senior engineering role that demands strong execution, adaptability, and comfort working at the pace of evolving AI technologies
Key Responsibilities
Productize and Optimize AI Services:
- Translate prototypes into production-ready microservices using Java, Kotlin, Python or TypeScript, deployed via Kubernetes and managed through CI/CD pipelines.
- Implement event-driven services using Kafka, SNS/SQS, or Event Bridge to support loosely coupled, fault-tolerant communication across services.
- Build and maintain APIs with clear contracts, emphasizing reliability, observability, and long-term maintainability.
Lead, Collaborate, and Mentor:
Contribute to technical planning, system design, and delivery reviews across engineering and product.
Work directly with platform teams, product managers and ML engineers to deliver business-ready AI capabilities.
Mentor team members and stay current on emerging trends in LLM tooling, orchestration, and agent frameworks.
Design & Build AI Middleware:
- Design and build API gateways, wrappers, and middleware services that expose studio systems and data to AI agents.
- Manage secure access to tools, metadata, and content systems through model context protocol (MCP).
- Standardize how agents’ interface with internal services across the supply chain.
Build Agentic Workflows:
- Deploy agentic platforms such as LangGraph or LangChain to manage tool execution, context routing, and decision logic.
- Implement tracing, audit logging, and governance checkpoints including human approval and fallback paths.
- Integrate orchestration into media workflows such as mastering, localization, and delivery.
Required Qualifications
- Bachelor's or master's degree in a Computer Science, Software Engineering, or related field.
- 8+ years of backend or systems engineering experience, with at least 3 years building AI-powered or workflow-centric platforms.
- Deep proficiency in Java or Kotlin in a backend environment, with deep understanding of testing, observability, and systems at scale.
- Clear, structured communication skills and a track record of technical leadership in cross-functional teams.
- Production experience with cloud platforms such as AWS, and container orchestration with Docker and Kubernetes.
- Hands-on experience deploying infrastructure via Terraform, CDK, or similar infrastructure-as-code frameworks.
- Experience building or integrating workflow engines, ideally in LLM or GenAI contexts.
Preferred Qualifications
- Experience with LLM frameworks and APIs such as LangGraph, LangChain, OpenAI, or Anthropic.
- Background in media, entertainment, or content operations, including localization or post-production workflows.
- Experience with the Spring ecosystem.
- Experience building multi-tenant or shared platform services.
- Exposure to Agile delivery practices and DevOps automation at scale.
Contributions to GenAI open-source projects or internal AI/ML infrastructure tooling.
