AI Developer

Noblesoft Technologies; California; On-site; USD 140,000 - 220,000; Full time

Job summary

Noblesoft Technologies Inc. is seeking an AI Developer to build and operate AI-enabled applications across customer experiences, productivity, and operations. You will own end-to-end delivery, from design to production, focusing on secure integrations and observable systems.

In this production engineering role, you will implement LLM-based solutions, build APIs, manage latency and cost, and collaborate with data, security, and product teams to deliver reliable, scalable AI capabilities.

Qualifications

• Typically 4+ years in professional software engineering, delivering AI/ML apps to production.
• Hands-on experience with LLM APIs, prompt and context design, RAG, embedding/search systems, 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.

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 behavior, 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.

Skills

LLM APIs

Prompt design

RAG

Embedding/search

Tool calling

Automated evaluation

CI/CD

Cloud services

Observability

Production ownership

Education

Bachelor's degree in computer science, engineering, data science, or related field

Tools

SQL

Docker

Kubernetes

CI/CD pipelines

Cloud services

Containers

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

• 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 behavior, 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 behavior, safety, robustness, latency, and cost.
• Run regression testing across prompt, model, retrieval, tool, and policy changes; analyze 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 4+ 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.