A Natural Language Processing Engineer specializes in developing and implementing algorithms and models tailored for understanding, processing, and generating natural language text. They utilize methodologies such as tokenization, parsing, named entity recognition, part-of-speech tagging, and other NLP techniques to perform tasks including text classification, chatbot development, and other applications where the primary input or output is natural language text.
AI Engineer FalconSmartIT Los Angeles, CA Job Details Contract 4 hours ago Qualifications AI models Containerization systems Version control Software engineering Continuous Delivery (CD) implementation Enterprise software IT system monitoring Generative models Application deployment AI platforms (beyond public GPTs) Prompt engineering SQL Machine intelligence Model deployment Natural language processing Production monitoring DevOps automation Model evaluation Design (software development lifecycle) MLOps Feature engineering Generative AI Cloud services Web services design System performance monitoring
Full Job Description Job Title:
AI Engineer Location:
Los Angeles, CA (Onsite)
Job Type:
Contract Job Description :
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 AI application engineering:
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.
Team delivery:
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.
