Staff AI Engineer

Staff AI Engineer

Disney Entertainment and ESPN Product & Technology

Technology is at the heart of Disney’s past, present, and future. Disney Entertainment and ESPN Product & Technology is a global organization of engineers, product developers, designers, technologists, data scientists, and more – all working to build and advance the technological backbone for Disney’s media business globally.

The team marries technology with creativity to build world-class products, enhance storytelling, and drive velocity, innovation, and scalability for our businesses. We are Storytellers and Innovators. Creators and Builders. Entertainers and Engineers. We work with every part of The Walt Disney Company’s media portfolio to advance the technological foundation and consumer media touch points serving millions of people around the world. 

Here are a few reasons why we think you’d love working here:

• Building the future of Disney’s media: Our Technologists are designing and building the products and platforms that will power our media, advertising, and distribution businesses for years to come.

• Reach, Scale & Impact: More than ever, Disney’s technology and products serve as a signature doorway for fans' connections with the company’s brands and stories. Disney+. Hulu. ESPN. ABC. ABC News…and many more. These products and brands – and the unmatched stories, storytellers, and events they carry – matter to millions of people globally. 

• Innovation: We develop and implement groundbreaking products and techniques that shape industry norms, and solve complex and distinctive technical problems.

Ad Platforms – AI Engineering

Ad Platform builds Disney’s industry-leading advertising technology, supporting sports, news, and entertainment content across all major media platforms. Our systems power high-scale, high-throughput, revenue-critical workloads that operate at massive enterprise scale.

We are looking for Staff Software Engineers to drive the technical evolution of AI-powered advertising systems in Bengaluru. These roles offer deep architectural ownership, global visibility, and the opportunity to influence mission-critical AI systems that serve millions of users worldwide.

This is a hands-on technical role for engineers who thrive on solving complex distributed systems challenges and shaping long-term architecture. You will stay close to design and implementation, contributing high-quality code while guiding architectural decisions across teams. You’ll influence engineering direction through strong technical judgment, mentor other engineers through design reviews and technical coaching, and lead complex initiatives that scale AI-enabled products and shared capabilities.

You will play a key role in modernizing cloud-native systems, advancing AI orchestration and reliability practices, and ensuring AI workloads are scalable, observable, and production-ready. You’ll also champion responsible and effective use of artificial intelligence to improve system quality, operational resilience, and developer productivity.

Role Summary

We are hiring Staff Engineers to lead the design and implementation of AI-enabled systems across software products, shared capabilities, and AI systems operations within Ad Platforms.

In this highly hands-on role, you will design and build AI-powered software products, reusable shared AI components, and production-grade AI services operating at enterprise scale. You will influence architectural direction across teams, ensuring AI solutions are scalable, reliable, and built for long-term evolution.

You will partner closely with product, data, infrastructure, and AI operations teams to move AI systems from experimentation to robust production environments. Success in this role is measured by architectural impact, technical depth, cross-team influence, and the ability to raise engineering standards across AI domains.

What You’ll Do

Architect and build AI-powered software products

Lead the design and implementation of AI-enabled web applications and backend services that orchestrate LLMs and multimodal foundation models. Establish scalable patterns using Python and structured orchestration frameworks such as LangGraph.

Drive reusable AI shared capabilities

Design and implement common AI components, orchestration patterns, and model integration services adopted across multiple engineering teams. Ensure systems are modular, extensible, secure, and observable.

Advance production-grade AI systems

Define and implement reliability, evaluation, monitoring, and cost-aware engineering practices for AI workloads. Contribute to model evaluation frameworks, release gating processes, and operational telemetry to ensure measurable performance and stability.

Influence technical direction

Lead complex design reviews, guide architectural decisions, and mentor engineers through technical leadership. Identify systemic improvements in scalability, performance, and operational maturity across AI initiatives.

Collaborate across domains

Partner with product, data, infrastructure, and governance teams to deliver AI systems that balance innovation, reliability, and responsible usage.

What makes this role unique

• Global impact from India: You’ll be part of Disney India innovation hub and work with talented builders creating impact on customers across geography. You will lead AI platforms that power global products like Disney+, Hulu, ESPN, and ABC—used by millions of people worldwide.

• Strong ownership: Disney India is a core engineering hub. Ad Platform leaders have complete ownership and autonomy of architecture, delivery, and people development.

• Leadership development: Build and mentor senior AI engineers and managers, shaping the future of Disney’s engineering leadership in India.

• Work-life harmony: Disney India values work-life harmony. We provide flexibility as part of work culture, including flexible workdays and hours.

Basic Qualifications

• 8+ years of professional software engineering experience building scalable distributed systems.

• Strong hands-on experience building and operating AI-enabled applications or services in production environments.

• Experience integrating LLMs or multimodal foundation models into real-world systems.

• Strong proficiency in Python and experience designing structured AI workflows using orchestration frameworks such as LangGraph, LangChain, or similar.

• Experience designing API-driven architectures and integrating model-serving or external services across cloud environments.

• Strong understanding of foundation model behavior, including latency characteristics, rate limits, cost dynamics, and performance tradeoffs.

• Experience driving system design and architectural decisions that influence multiple teams.

• Strong engineering fundamentals including testing, CI/CD, and maintainable code practices.

• Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent practical experience.

Preferred Qualifications

• Experience leading large, cross-team technical initiatives in AI-enabled or distributed systems environments.

• Experience building reusable AI capabilities adopted across multiple product teams.

• Experience contributing to reliability engineering, evaluation frameworks, or operational maturity initiatives.

• Experience working on high-throughput, revenue-impacting systems at scale.

• Familiarity with responsible AI considerations, evaluation metrics, and system governance patterns.

• Strong ability to influence technical direction without formal management authority.

• Experience implementing retrieval-augmented generation (RAG) systems using embeddings and vector search.

• Experience designing prompt strategies and model interaction patterns to optimize quality, latency, and cost.

• Familiarity with secure model integration practices, including prompt injection mitigation and output validation.

Experience With

• Designing and scaling distributed AI systems that orchestrate LLMs and multimodal models at production scale.

• Implementing structured agent architectures, evaluation loops, and performance validation techniques.

• Establishing observability for AI workloads, including monitoring latency, throughput, cost, and model behavior.

• Designing secure, scalable REST or event-driven APIs in cloud-native environments.

• Applying object-oriented design principles and modular architectures to support long-term maintainability.

• Driving operational improvements across reliability, cost efficiency, and system performance

• Retrieval pipelines, embeddings, and vector database integration.

• Multi-model orchestration, routing, and fallback strategies.

• Token optimization, caching strategies, and cost-performance tuning for AI workloads.