Company Description
NBCUniversal is one of the world's leading media and entertainment companies, creating world-class content distributed across film, television, and streaming, and brought to life through global theme park destinations, consumer products, and experiences. We own and operate leading entertainment and news brands, including NBC, NBC News, NBC Sports, Telemundo, NBC Local Stations, Bravo, and Peacock. We produce and distribute premier filmed entertainment and programming through powerhouse film and television studios, including Universal Pictures, DreamWorks Animation, and Focus Features, and the four global television studios under the Universal Studio Group banner, and operate industry-leading theme parks and experiences around the world through Universal Destinations & Experiences. NBCUniversal is a subsidiary of Comcast Corporation. Our impact is rooted in improving the communities where our employees, customers, and audiences live and work, with a rich tradition of giving back and ensuring employees have opportunities to serve their communities. We champion an inclusive culture and strive to attract and develop a talented workforce to create and deliver a wide range of content reflecting our world.
Job Description
The forward-deployed AI Engineer designs, builds, and ships AI-powered solutions, including agentic systems, that transform workflows and unlock new capabilities across NBCUniversal News Group. This role is deeply technical, highly collaborative, and execution-focused, with success measured by production adoption and measurable operational impact.
This individual works end-to-end from discovery and prototyping through deployment and iteration, combining LLMs, retrieval, data integration, and orchestration frameworks to deliver reliable solutions in production. This is not a backend-only role; the engineer will build across services, integrations, and user-facing experiences as needed. Engineers will work closely with editorial, product, data, and technology teams to co-create solutions in a fast-moving, real-time business environment where speed and timely delivery are critical.
Key Responsibilities:
Design, Build, and Ship AI + Agentic Solutions
- Architect and implement end-to-end AI applications from concept to production, including agentic workflows.
- Build systems using LLMs and multimodal models, retrieval and vector databases, and orchestration frameworks (e.g., LangChain/LangGraph, Google ADK), integrated with internal tools and data.
- Own the full lifecycle: discovery, prototyping, validation, deployment, monitoring, iteration, and support.
Execute Fast in a High-Velocity Environment
- Deliver solutions on accelerated timelines aligned to editorial and business urgency.
- Prioritize evidence-driven iteration over perfection; make clear tradeoffs across scope, speed, and quality.
- Adapt quickly to changing requirements and real-world feedback, keeping stakeholders aligned.
Hands-On Technical Execution
- Write production-quality code and integrate APIs across internal platforms and external providers (e.g., Google, OpenAI, Databricks, AWS, Microsoft).
- Make pragmatic architectural decisions balancing speed, scale, security, reliability, and cost.
- Build reusable components, templates, and playbooks that accelerate future development and reduce friction for downstream teams.
Apply the Right AI to the Right Problems
- Evaluate model capabilities and select appropriate AI tools based on workflow needs, risk profile, and operational constraints.
- Combine services across selected providers into cohesive, reliable systems with strong observability and clear failure modes.
- Implement evaluation frameworks to measure quality, reliability, and impact, and to guide iteration.
Responsible AI, Safety, and Governance
- Design guardrails for agentic systems, including tool safety, permissioning, data handling, and human-in-the-loop review where needed.
- Partner with Standards, Legal, Security, and GRC to ensure compliant data use, responsible deployment, and audit-ready practices.
- Establish monitoring and incident response patterns to detect issues and maintain trust and reliability at scale.
Collaborate Deeply Across Teams
- Work directly with editorial, product, and business partners to shape solutions from requirements through rollout.
- Collaborate with our Data team on data quality, retrieval strategies, evaluation design, and measurement.
- Partner with the CTO and platform engineering organizations to harden solutions for production environments.
- Communicate clearly with technical and non-technical audiences, translating complexity into decisions and action.
Stay Ahead of the Market
- Track new model releases, vendor updates, and emerging patterns in agentic AI.
- Run targeted experiments with new capabilities and share timely recommendations that influence roadmaps and standards.
Leverage AI in the Role
- Use AI coding agents and copilots to accelerate development, debugging, refactoring, test generation, and documentation.
- Apply AI to speed analysis, coordination, and knowledge capture while maintaining security, quality, and compliance.
Qualifications
- 4+ years of professional software engineering experience (or equivalent).
- Proven experience shipping production systems (not just prototypes).
- Strong coding skills in Python and/or TypeScript/JavaScript; comfortable with APIs and system integration.
- Hands-on experience with LLM-powered applications (agentic patterns, prompting, orchestration/agents, evaluation, deployment considerations).
- Demonstrated ability to effectively use AI coding agents (e.g., agentic IDE tools) to speed delivery while ensuring correctness, testing, maintainability, and secure coding practices.
- Strong communication skills with technical and non-technical stakeholders.
Desired Characteristics:
- Experience with LangChain and/or LangGraph for orchestrating agentic workflows (routing, state machines, multi-agent patterns, tool execution).
- Experience with Google ADK (or comparable agent development kits) for building and operationalizing agents.
- Familiarity with cloud infrastructure (AWS/GCP/Azure), observability, and production operations.
- Exposure to model evaluation concepts and responsible AI practices.