AI Engineer

AI Developer will build and operate AI-enabled applications for customer experiences, employee productivity, and operations. Use cases may include conversational support, knowledge assistance, search and discovery, summarization, classification, decision support, workflow automation, and content/metadata operations. This is a production engineering role. Success requires strong software fundamentals, disciplined evaluation, secure enterprise integration, and ownership of quality, latency, cost, observability, and supportability throughout the application lifecycle. Key responsibilities ? Build production applications using large language models, smaller task-specific models, retrieval-augmented generation, tool/function calling, workflow orchestration, and deterministic business logic where appropriate. ? Develop secure APIs, services, adapters, and event-driven integrations for digital channels, customer-care platforms, enterprise knowledge, billing and entitlement services, content/metadata systems, and internal workflows. ? Implement authorization-aware tool use, input validation, idempotency, timeouts, retries, fallback behaviour, circuit breakers, and human escalation paths. ? Choose prompts, retrieval, rules, conventional machine learning, or fine-tuning based on evidence rather than defaulting every problem to a large model. Retrieval, data, and grounding: ? Build ingestion, chunking, metadata, indexing, retrieval, reranking, citation, freshness, and deletion workflows for enterprise knowledge and approved content sources. ? Preserve source permissions and customer/data boundaries throughout retrieval and generation; prevent unauthorized cross-user, cross-account, or cross-domain disclosure. ? Partner with Data Engineering and domain owners on data quality, system-of-record alignment, lineage, and feedback loops. Evaluation and quality engineering ? Create representative evaluation datasets and automated test suites for groundedness, relevance, correctness, task completion, refusal behaviour, safety, robustness, latency, and cost. ? Run regression testing across prompt, model, retrieval, tool, and policy changes; analyse failure modes and improve the system using trace-based evidence. ? Instrument online quality and business metrics, support controlled experiments, and incorporate human review for higher-risk or lower-confidence outcomes. Production operations and MLOps ? Build CI/CD pipelines for code, configuration, prompts, evaluation assets, and model or index changes across separated development, test, and production environments. ? Implement structured logging, tracing, token and infrastructure cost monitoring, model/provider health checks, alerting, dashboards, and operational runbooks. ? Optimize throughput, latency, reliability, and cost using caching, batching, routing, prompt/context management, and appropriately sized models. ? Participate in production support, incident response, root-cause analysis, and continuous improvement. Security and responsible implementation: ? Implement controls for prompt injection, jailbreak attempts, unsafe tool use, data leakage, malicious content, model abuse, and dependency/supply-chain risk. ? Apply DIRECTV requirements for PII and payment-card data, identity and access, secrets management, retention, content rights, audit logging, and approved model/provider use. ? Contribute reusable components to the AI control plane, including policy enforcement, prompt/model configuration, evaluation hooks, telemetry, and kill-switch or rollback mechanisms. ? Work with Product Managers, UX, Solution Architects, AI Architects, Data Engineers, Cybersecurity, Quality Engineering, and Operations to deliver testable user outcomes. ? Write maintainable code, automated tests, interface contracts, technical documentation, deployment guides, and operational runbooks; participate in code and design reviews. Required qualifications: Typically, 10+ years in professional software engineering, including meaningful hands-on experience delivering AI, machine-learning, search, NLP, or data-intensive applications to production; equivalent experience is welcome. Hands-on experience with LLM APIs, prompt and context design, RAG, embedding/search systems, structured outputs, tool/function calling, and automated evaluation. Experience with SQL and document/search stores, containers, CI/CD, source control, cloud services, and observability practices. Strong software engineering habits: modular design, automated testing, secure coding, peer review, performance troubleshooting, and production ownership. Ability to explain model limitations and engineering trade-offs to technical and nontechnical partners. Bachelor's degree in computer science, engineering, data science, or a related field, or equivalent practical experience. #J-18808-Ljbffr