Sr. Software Engineer, Automation Eng

NBCUniversal•New York, NY

2d•$140,000 - $180,000•Remote

About The Position

NBCUniversal Advertising Products & Solutions (AP&S) is responsible for the product development and project management of NBCUniversal’s full advertising technology suite. From sales support to campaign execution, delivery, and billing, our division services both internal and external customers in support of NBCUniversal’s $7B+ annual advertising business. Together, we’re building the platform that powers the future of advertising at NBCU. Our Data Collaboration Group, a core part of AP&S, powers the full advertiser lifecycle across insights, measurement, onboarding, activation, and optimization, utilizing various integration pathways including Data Clean Rooms and secure data shares. Within the Data Collaboration Group, our Engineering & Operations team builds the automation and platform capabilities behind these workflows. We are seeking a Senior Software Engineer, Automation Engineering to join the Engineering & Operations team. Reporting to the Director of Automation Engineering, this role will build AI-enabled automation systems and production-grade software that support NBCUniversal’s data collaboration ecosystem. The ideal candidate is a strong Python engineer who can design reliable systems, productize repeatable workflows, and build AI agents that help automate complex engineering and operational tasks across data platforms for audience activation, measurement, and reporting.

Requirements

Bachelor’s degree or equivalent practical experience in Computer Science, Information Systems, Software Engineering, Electrical Engineering, Electronics Engineering, or a related technical field
5+ years of experience building production-grade software using Python, including libraries, services, testing, CI/CD, and code reviews
Hands-on experience building applications using LLMs, RAG, vector databases, prompt engineering, or frameworks such as LangChain, LangGraph, LlamaIndex, or similar tools
Experience designing AI-enabled workflows, reasoning agents, tool-using agents, or complex automation systems
3+ years of hands-on experience with cloud data platforms such as Snowflake, Databricks, or similar technologies
Strong understanding of production system design, including observability, reliability, scalability, performance tuning, and operational support

Nice To Haves

Exposure to data clean room concepts or platforms such as Snowflake Clean Rooms, Databricks Clean Rooms, LiveRamp, Habu, or similar technologies
Experience with advertising technology, audience activation, campaign delivery, reach and frequency, attribution, incrementality, or reporting workflows
Snowflake SnowPro Core Certification, Databricks Certified Data Engineer Associate, or similar cloud/data platform certification

Responsibilities

Design and build internal AI agents and automation workflows using technologies such as, Snowflake Cortex, LangChain, LangGraph, or similar frameworks, to support planning, tool use, retrieval, validation, and human-in-the-loop execution where appropriate.
Develop reusable tools, APIs, and components that engineers can compose into new agentic workflows.
Implement retrieval-augmented generation workflows, context management strategies, and prompt patterns that improve accuracy, reliability, latency, and cost efficiency.
Build evaluation harnesses, regression tests, and monitoring patterns for AI-agent behavior.
Define and track metrics such as task completion, groundedness, response accuracy, latency, cost, and failure rate.
Design guardrails and validation patterns to reduce hallucinations, unsafe outputs, and unreliable automation behavior.
Partner with engineering and operations teams to move AI workflows from prototype to production-ready systems.
Design, build, and maintain production-grade Python applications, libraries, and services.
Champion object-oriented design principles, including encapsulation, abstraction, inheritance/composition, reusable interfaces, and clean separation of concerns to improve maintainability and extensibility.
Champion software engineering best practices including modular design, automated testing, CI/CD, code reviews, observability, and documentation.
Create reusable engineering patterns that reduce bespoke development effort and improve consistency across partner engagements.
Collaborate with product, engineering, operations, and data platform teams to translate repeatable business needs into scalable technical solutions.
Build reusable Python libraries that support clean room capabilities across first-party audience workflows and core measurement use cases, including audience onboarding, ingestion, indexing, activation, campaign and impression delivery analysis, reach and frequency, attribution, and incrementality.
Abstract complex analytical and data collaboration workflows into repeatable, self-service components for internal teams and external partners.
Enable configurable feature deployment so new audience and measurement capabilities can be delivered quickly and consistently across partners.
Mentor engineers through code reviews, technical design discussions, and operational best practices.
Help establish engineering standards for AI-assisted workflows, agentic system design, reusable libraries, and production automation.
Promote a culture of reliability, maintainability, and continuous improvement.