System Engineer 2, Full Stack Applied AI, AdTech

DIRECTV•New York, NY

3d•$134,516 - $201,653•Hybrid

About The Position

DIRECTV is seeking a System Engineer 2 with strong full stack software development and applied AI experience to build and deploy AI-powered products across our Advertising Technology ecosystem. This is a hands-on coding role with end-to-end responsibility for frontend experiences, backend services, APIs, AI integrations, data access, cloud deployment, and production support. The ideal candidate is product-minded and forward deployed. This person works directly with business users, turns ambiguous needs into working software, prototypes quickly, and then hardens successful concepts into secure, reliable, maintainable applications that deliver measurable business outcomes. This is a software development position, not an infrastructure administration, data science, or machine-learning model development role.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
5+ years of professional software engineering experience with substantial hands-on full stack development across frontend and backend systems.
Proven experience delivering customer-facing or internal enterprise applications from concept through production operation.
Practical experience integrating foundation models, agents, retrieval services, and intelligent automation into production software beyond isolated demos or notebooks.
Strong experience with TypeScript or JavaScript and a modern UI framework such as React or Next.js.
Experience building accessible, responsive applications with clear state management, component design, testing, and API integration.
Ability to create polished business workflows, not only chat interfaces.
Strong proficiency in Python and frameworks such as FastAPI; experience with Node.js, Java, Go, or C# is also valuable.
Experience designing REST or GraphQL APIs, service integrations, relational and NoSQL data stores, asynchronous processing, and distributed applications.
Hands-on experience with cloud services, containers or serverless deployment, authentication and authorization, CI/CD, testing, and observability.
Experience with foundation-model APIs or managed AI platforms and frameworks such as Bedrock, OpenAI, Anthropic, Snowflake Cortex, LangGraph, Semantic Kernel, or comparable technologies.
Working knowledge of RAG, embeddings, vector search, tool calling, agents, context engineering, structured output, prompt management, evaluation, and guardrails.
Proficiency both using and setting up AI cowork environments for business users, including workspace configuration, role-based access, agent and tool integration, shared context, reusable workflows, and user onboarding.
Ability to troubleshoot quality issues across prompts, context, tools, data, models, latency, and application behavior.
Ability to work directly with business users, demonstrate frequently, receive feedback constructively, and turn learning into better software.
Strong written and verbal communication with the ability to explain decisions and tradeoffs clearly.
High ownership, practical judgment, curiosity, and a bias toward delivering usable outcomes.

Nice To Haves

Experience in advertising, media, AdTech, MarTech, campaign operations, analytics, or other data-intensive business environments.
Experience building workflow, analytics, decision-support, conversational BI, or operational automation products.
Experience with AWS, Snowflake, Kubernetes, vector databases, feature flags, experimentation frameworks, and enterprise design systems.
Experience contributing to shared platforms, component libraries, developer tooling, or reusable AI application patterns.
Portfolio or concrete examples demonstrating ownership of frontend and backend production code. Experience limited primarily to prompt engineering, model experimentation, notebooks, analytics, or ML pipeline development does not meet the requirements of this role.

Responsibilities

Design, code, test, deploy, and operate complete AI-enabled applications, including responsive user experiences, backend services, APIs, data access, and AI orchestration.
Build intuitive conversational and workflow-based interfaces that help business users understand, refine, and act on AI-generated insights.
Set up, configure, and extend AI cowork environments that allow business users to collaborate with AI agents, enterprise knowledge, approved tools, and shared workflow context.
Own features end to end and produce maintainable, well-tested code suitable for enterprise production environments.
Apply sound software design, accessibility, performance, security, and usability practices across the application stack.
Build applications using LLMs, RAG, structured generation, tool calling, agents, semantic search, summarization, classification, and intelligent workflow automation.
Design prompts, context assembly, model routing, validation, guardrails, fallbacks, and human review for reliable business use.
Create evaluation datasets and automated tests for accuracy, groundedness, user value, safety, regression, latency, and cost.
Select the simplest effective approach, including deterministic software or traditional analytics when an LLM is not appropriate.
Partner directly with Ad Sales, Operations, Product, Data, and Engineering teams to observe workflows and identify high-value opportunities.
Translate incomplete requirements into prototypes, test them with users, and iterate rapidly based on evidence and feedback.
Connect technical choices to outcomes such as productivity, decision speed, quality, revenue protection, or reduced operational effort.
Support onboarding, adoption, documentation, training, and continuous improvement after launch.
Develop scalable backend services using modern API, microservice, asynchronous processing, and event-driven patterns.
Integrate applications with enterprise APIs, advertising platforms, Snowflake and other data stores, identity providers, content systems, and workflow tools.
Build secure tool interfaces with clear contracts, authorization, validation, observability, and error handling.
Implement caching, state management, retrieval, and data access patterns that improve quality, performance, and cost.
Implement CI/CD, infrastructure as code, automated testing, feature controls, monitoring, logging, tracing, alerting, and runbooks.
Diagnose issues across browsers, frontend applications, APIs, models, retrieval, data, cloud services, and external integrations.
Apply security, privacy, responsible AI, and data-governance requirements throughout design and delivery.
Contribute reusable components, development standards, and engineering accelerators.