Principal, System Engineer 2, Multi-Agent AI, AdTech

DIRECTV•New York, NY

3d•$178,952 - $268,307•Hybrid

About The Position

DIRECTV is seeking a Principal, System Engineer 2 to design, build, and operationalize sophisticated agentic AI systems across our Advertising Technology ecosystem. This is a senior individual contributor assignment for a hands-on systems engineer who can move between architecture, production code, platform services, and direct engagement with business users. The ideal candidate brings broad experience across AI, distributed systems, backend engineering, data, product delivery, and enterprise integration. This person will provide end-to-end technical ownership for secure multi-agent systems and collaborative AI work environments where business users and agents can plan, reason, use tools, share context, and complete complex advertising and media workflows.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
5 – 7 years of progressive software, systems, platform, or architecture experience, including hands-on ownership of production services.
Proven experience building and deploying agentic AI or GenAI systems beyond proofs of concept.
Demonstrated experience leading solutions from discovery through production and making end-to-end technical decisions.
Strong coding experience in Python and proficiency in at least one additional modern language such as TypeScript, Java, Go, or C#.
Deep knowledge of LLM applications, multi-agent orchestration, tool use, structured outputs, memory, RAG, context engineering, evaluation, and human-in-the-loop controls.
Strong background in backend engineering, API design, microservices, distributed systems, event-driven architecture, and cloud-native deployment.
Hands-on experience with frameworks and platforms such as LangGraph, Semantic Kernel, Strands, Bedrock AgentCore, Snowflake Cortex, or comparable technologies.
Experience implementing secure enterprise integrations, identity and authorization, observability, automated testing, and CI/CD.
Ability to explain complex system and agent architectures, integration decisions, risks, and tradeoffs to technical and non-technical audiences.
Proven ability to lead through influence, write and review production code, mentor engineers, and drive decisions across teams.
Product mindset with judgment to simplify designs and focus teams on measurable business outcomes.

Nice To Haves

Hands-on experience in advertising, media, AdTech, MarTech, campaign management, ad operations, programmatic advertising, measurement, or monetization.
Experience building cowork, collaborative agent, case-management, investigation, or human-agent work environments.
Experience across application engineering, platform engineering, data engineering, ML engineering, product development, SRE, or solution architecture.
Experience with AWS, Kubernetes, serverless services, Snowflake, vector or graph stores, and enterprise data platforms.
Open-source contributions, technical publications, patents, or demonstrated thought leadership in agentic systems.

Responsibilities

Design and build production multi-agent systems, including planners, coordinators, specialist agents, reviewers, human approval points, shared state, memory, and recovery paths.
Define orchestration patterns for agent delegation, task decomposition, consensus, tool selection, and deterministic workflow control.
Write production code and reference implementations, not just architecture diagrams, prototypes, or prompt specifications.
Evaluate model, framework, and runtime tradeoffs while keeping solutions modular and avoiding unnecessary vendor lock-in.
Build collaborative workspaces where business users and multiple agents can jointly investigate, plan, create, review, and execute work.
Design shared-context patterns such as workspaces, case files, task state, artifacts, approvals, audit history, and role-aware memory.
Create experiences that make agent evidence, tool activity, decisions, and handoffs understandable to business users.
Enable reusable skills and workflows across campaign planning, operations, analytics, incident response, and engineering use cases.
Design and develop backend services, APIs, event-driven workflows, tool registries, and integration services that connect agents to enterprise capabilities.
Build governed tool interfaces using MCP-style patterns, strong schemas, identity propagation, authorization, validation, rate limits, and auditability.
Integrate agent systems with advertising platforms, data services, Snowflake, operational systems, content repositories, and third-party APIs.
Apply distributed-systems practices for reliability, idempotency, retries, timeouts, fallbacks, concurrency, and state management.
Design context strategies combining user intent, role, workflow state, enterprise knowledge, structured data, tool outputs, conversation history, and policy constraints.
Build retrieval and context pipelines that deliver the right information to the right agent at the right step while controlling noise, latency, cost, and sensitive data exposure.
Develop reusable instruction, skill, memory, and context templates for specific business personas and workflows.
Partner directly with business users to understand work, encode domain knowledge, and improve agent behavior through feedback and evaluation.
Establish evaluation strategies for task success, groundedness, tool correctness, trajectory quality, safety, latency, reliability, and cost.
Implement observability across agent steps, model calls, context assembly, tool activity, decisions, and business outcomes.
Design guardrails for unsafe actions, prompt injection, data leakage, runaway loops, failed tools, and low-confidence decisions.
Drive production readiness through automated testing, CI/CD, release controls, incident response, runbooks, and continuous improvement.
Work directly with Product, Ad Sales, Operations, Data, and Engineering teams to turn ambiguous problems into deployable agentic solutions.
Lead architecture and code reviews, mentor engineers, establish reusable standards, and manage cross-system dependencies and risks.
Balance rapid experimentation with maintainability, security, governance, and measurable business value.