AI Engineer

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.

• 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., Lang Chain/Lang Graph, 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.

• 4+ years of professional software engineering experience (or equivalent).
• Proven experience shipping production systems (not just prototypes).
• Strong coding skills in Python and/or Type Script/Java Script ; 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.

• Experience with Lang Chain and/or Lang Graph 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.