Principal Architect-Strong experience with the C4 model

Principal Architect
Location: US Remote

Long Term

OVERALL SUMMARY
As part of the Operations & Technology Content Intelligence team, Staff AI Engineers are responsible for the design and maintenance of systems to support reliability of numerous workflows for NBCUniversal’s broadcast and streaming channels. This includes internal business applications, new technology initiatives, and consumer-facing systems.  A successful Staff AI Engineer will be able to build full-stack AI product experiences across every layer of the stack, has shipped production software, understands modern AI systems beyond simple API integration, and is comfortable working across backend services, agentic workflows, evaluation systems, retrieval infrastructure, and user-facing product experiences.
 
RESPONSIBILITIES
Experience building agentic harnesses from scratch, including capabilities such as tool use, multi-step chaining, reasoning, streaming, skills, multimodal integration, RAG, sandboxing, and state management.
Experience designing, building, and operating production APIs and services, including RESTful APIs, streaming APIs, asynchronous workflows, service boundaries, versioning, authentication/authorization, error handling, and backward compatibility. Strong judgment around when to use synchronous APIs, event-driven architectures, queues, background jobs, or streaming protocols based on latency, reliability, scalability, and user experience requirements.
Demonstrated ownership of production systems, including process improvements, roadmap contributions, defect resolution, uptime and availability monitoring, and operational reliability.
Strong backend engineering fundamentals, with comfort working across service design, APIs, batch workflows, orchestration, observability, and production support.
Familiarity with evaluation techniques such as creating and maintaining evaluation datasets, running offline regression evals, monitoring online production performance, rubric-based scoring, self-verification, human-in-the-loop review, AI-as-judge methods, and quality/reliability analysis at scale.
Clear understanding of foundational machine learning and statistical concepts, including sampling, statistical significance, overfitting/underfitting, precision and recall, and quality tradeoff analysis.
Experience building and orchestrating large-scale batch workflows that use foundation models, including LLMs and VLMs, as well as pretrained open-source models and deep learning models.
Ability to design systems that safely and reliably automate meaningful enterprise workflows using non-deterministic AI components.
Strong judgment around reliability, failure handling, observability, human review, and operational safety.
Deep understanding of how to make systematic tradeoffs between quality, reliability, latency, cost, explainability, and user experience in complex AI systems.
Ability to design pragmatic architectures that balance innovation with production readiness.
Comfort working in environments where model behavior is non-deterministic and system design must account for uncertainty, evaluation, monitoring, and graceful failure.
Experience with both lexical and embedding-based search methods.
Ability to reason about relevance, ranking, latency, recall, precision, indexing strategy, and retrieval performance.
Experience working with foundation models and open-source LLMs beyond simple API calls.
Familiarity with lower-level model behaviors and controls, including temperature, top-p sampling, logprobs, confidence scoring, prompting strategies, and model selection tradeoffs.
Comfort and willingness to build front-end experiences using JavaScript/TypeScript and frameworks such as React.
While the role is primarily backend and AI systems focused, the ability to contribute to user-facing product experiences is important.
Willingness to work across the stack and own the end-to-end product experience is essential.
Familiarity with tools such as FFmpeg, OpenCV, or similar media-processing libraries is a plus.
 
QUALIFICATIONS/REQUIREMENTS
7+ years of a programming languages such as Java, Golang, Python, JavaScript
2+ years of experience architecting, designing, and optimizing search and information retrieval systems at scale.
6 months to 1 year of hands-on experience building agentic products or solutions, including tool-using agents, conversational agents, long-running agents, reasoning/planning agents, or similar systems.
Experience with multiple modern agent frameworks is preferred. Specific frameworks are less important than demonstrated fluency, but exposure to tools such as OpenAI Agents SDK, LangGraph, Google ADK, SmolAgents, or comparable frameworks is valuable.
1+ year of experience evaluating AI systems for quality, reliability, and safety at scale.
5+ years of experience shipping production software in an enterprise or consumer environment, not just prototypes or proofs of concept.
OpenSearch or Elasticsearch experience is preferred, but comparable experience with other search and retrieval systems is sufficient.
Expert experience, understanding and knowledge of digital and broadcast production operations and workflows
Experience working with video, rich visual media, or multimodal AI systems.
Strong knowledge of industry trends and best practices
Strong experience with the C4 model and other traditional design artifacts
Experience working in an Agile environment
 
DESIRED CHARACTERISTICS
 
Experience with training, fine-tuning, or adapting models for domain-specific use cases using techniques such as LoRA, PEFT, or related approaches.
Exposure to stateful software design patterns and streaming protocols such as WebSockets
Familiarity with client-side web technologies (React, Angular, JavaScript, CSS, HTML)
5+ years working with the AWS cloud
1+ years working with the Azure cloud
Familiarity with continuous integration/delivery practices
Docker, Kubernetes experience a plus
BS in CS, EE or equivalent experience required