retrieval-augmented-generation jobs in San Diego

Design, build, and deploy production agentic AI solutions powered by Claude for enterprise clients, from proof-of-concept through production. Implement multi-agent systems that automate business processes, integrate with enterprise tools, and operate within defined autonomy and guardrails.…See more Modernize existing AI/ML platforms with generative AI capabilities including RAG and agent-based orchestration. Advise clients on generative AI adoption strategy, responsible AI practices, and model selection while partnering with technology, data, and business stakeholders to scope and deliver solutions.

Lead enterprise generative AI architecture and solution design on Oracle Cloud Infrastructure (OCI), focusing on agentic workflows, retrieval-augmented generation pipelines, and Oracle application integrations. Define governance, security, and compliance standards for AI/ML initiatives across the enterprise.…See more Mentor delivery teams on prompt engineering, model selection, and optimization; identify high-value AI use cases and guide pilot deployments that drive business outcomes.

Senior Wireless Software Engineer role focused on embedded systems and real-time firmware development for DSP and Arm architectures. Requires deep expertise in modern C/C++, operating systems internals (RTOS), data structures, and embedded debugging techniques.…See more The role uniquely emphasizes AI-native engineering practices, including using AI coding assistants and agentic tools to augment the development workflow, managing AI-assisted code generation and test synthesis, and critically evaluating AI output for safety and correctness in real-time constraints.

Lead ServiceNow AI implementation projects as an architect-manager, designing and deploying AI solutions (NOW Assist, Agentic, AI Control Tower) with hands-on expertise in prompt engineering, LLM orchestration, and RAG techniques. Configure Moveworks for automated ticket resolution and integrate multiple AI models within ServiceNow workflows.…See more Conduct client design sessions, manage project delivery across waterfall and agile models, and lead technical teams while supporting pre-sales efforts.

Senior AI/ML Engineer responsible for shipping end-to-end production ML systems that enable autonomous medical coding in a SaaS platform. Will design robust ML architectures with low-latency inference, build reliable data and model pipelines using modern MLOps practices (CI/CD, model registries, experiment tracking), establish evaluation strategies with statistical rigor, and implement model observability and governance including drift detection and bias assessments.…See more Requires 5+ years of industry experience building and operating ML systems in production, 3+ years with C# or Python, 3+ years on Azure, and 1+ years of LLMOps expertise including prompt engineering and RAG.

Design, develop, and optimize machine learning systems for production AI platforms, focusing on model development, inference optimization, and scalable ML infrastructure. Responsibilities include building ML pipelines and frameworks, optimizing model inference for latency and cost, integrating LLMs into APIs and microservices, and designing data pipelines for preprocessing and feature engineering.…See more The role requires strong software engineering fundamentals combined with deep ML expertise, working across PyTorch/TensorFlow, model-serving systems, distributed computing, and GPU environments.

Build and ship production ML systems end-to-end for autonomous medical coding in a SaaS platform, from problem framing through deployment and monitoring. Own the full lifecycle—data discovery, feature engineering, training, evaluation, and iteration—working with LLMs, deep learning, and modern MLOps tooling to handle millions of medical charts daily.…See more Design robust, low-latency architectures, establish rigorous evaluation strategies, and implement model observability, drift detection, and bias assessments. Collaborate across product, data, and platform teams to translate business goals into measurable ML outcomes.

Design, implement, and optimize real-time embedded modem firmware across protocol layers and drivers while meeting strict latency, memory, and power constraints. You will own end-to-end feature delivery from requirements through on-target bring-up, apply rigorous data-structure and algorithm design, and pioneer AI-native development practices by integrating AI coding agents, LLM-based code review, and automated test synthesis into daily workflows.…See more Debug complex multi-layer issues on target hardware using JTAG, logic analyzers, and AI-assisted log/crash analysis. Mentor peers on embedded fundamentals and AI-native engineering techniques while contributing to design reviews and engineering standards.

Principal System Architect responsible for designing and maintaining a complex enterprise automation ecosystem spanning RPA, Machine Learning, and GenAI across multiple business units. The role requires architecting data pipelines integrating diverse systems (SAP, ERP, QMS, PLM tools like Windchill), leading automated workflow solutions (ECPs, QA defect prediction, EVMS reporting), and ensuring end-to-end cybersecurity compliance with DoD standards, CMMC, and Zero Trust architectures.…See more This is a senior technical leadership position requiring 14+ years of IT system architecture experience, deep expertise in intelligent automation and ML/AI, and the ability to mentor teams and present technical strategies to executive stakeholders.

The AI Learning Architect designs and implements generative AI applications for learning and development, using prompt engineering, retrieval-augmented generation, and low-code automation to personalize training content and remove manual steps. They structure learning data into modular frameworks, evaluate and integrate AI tools with existing learning platforms, set up analytics tracking via xAPI, and debug prompt configurations to keep solutions accurate and current.…See more The role requires 3+ years in instructional design or educational technology with hands-on AI integration, proficiency in modern generative AI models and low-code tools, knowledge of learning frameworks (ADDIE, SAM), and the ability to translate technical AI behavior into accessible user experiences. They embed responsible AI practices including data privacy, bias checks, and accessibility from the start, while documenting workflows and coaching teammates.

Mid-level software engineer building AI/ML capabilities for a defense customer. You'll develop NLP and machine learning pipelines in Python (text analytics, topic modeling, embeddings, entity extraction), design knowledge graphs and network-analysis tools, develop collaborative TypeScript front-end applications, and deploy APIs and production systems.…See more Requires 4+ years Python development or equivalent front-end experience plus 2 years Python, hands-on NLP/ML experience, and ability to work with limited supervision.

Senior Data Engineer at Sony to design, build, and maintain scalable data pipelines, dashboards, and reporting solutions supporting PlayStation's payments, subscription, and fraud-prevention systems. Will develop AI-ready data products for analytics, automation, and machine-learning workflows in close partnership with data scientists, analysts, and global payments stakeholders.…See more Requires 7+ years in data engineering or related software/systems work, strong SQL and data modeling, proficiency in Python/Java/scripting languages, and experience operationalizing data for ML and governance. Will mentor team members, communicate technical architectures to executives, and drive platform reliability and security.

This role involves conducting comprehensive security assessments of AI systems and deployments, evaluating them against NIST frameworks (AI RMF, CSF, 800-53, 800-171) and other security standards. The consultant will identify AI-specific risks such as prompt injection, model misuse, data leakage, and supply-chain vulnerabilities across cloud platforms (AWS, Azure, hybrid).…See more Responsibilities include developing AI governance frameworks, policies, and controls; assessing AI development environments, model pipelines, APIs, and orchestration layers; and delivering executive-level recommendations and remediation roadmaps. Requires 7+ years of cybersecurity/consulting experience and 3+ years supporting AI/machine learning security initiatives, with strong expertise in AI threat modeling, secure AI deployment, and governance frameworks.

Lead large-scale enterprise data transformation programs at Qualcomm, managing multi-year initiatives across data modernization, cloud migration, and analytics platforms. Coordinate work across distributed data engineering, architecture, governance, security, and analytics teams while managing multi-million-dollar budgets and vendor relationships.…See more Drive adoption of modern data architectures (data lakehouse, data mesh, medallion patterns) and cloud platforms (Databricks, AWS, Azure, GCP), and establish federated data governance frameworks. Influence executives and technical stakeholders to deliver measurable business outcomes while handling legacy system retirement and enterprise data integration from CRM, ERP, HCM, and supply-chain systems.

Cloudflare seeks a Senior Customer Engineer to serve major accounts in the Southwest, acting as a trusted technical advisor on enterprise network and security architectures. The role combines deep consultative skills with hands-on technical expertise across Internet fundamentals, security, performance, and developer platforms—translating complex technical concepts into business outcomes for both engineering teams and C-suite executives.…See more The engineer will demonstrate architectural breadth in at least four domains (OSI layers, BGP, WAF, Zero Trust, caching, AI/LLM workflows, scripting, or cloud-native development) and drive customer adoption through technical enablement, custom integrations, and solutions design. This is a quota-carrying, revenue-aligned customer-facing role requiring exceptional communication, SaaS business acumen, and the ability to navigate full-lifecycle enterprise sales cycles.

Sr. Engineer responsible for designing, building, and deploying production-grade AI platforms and solutions centered on LLMs, RAG, and agentic workflows.…See more The role spans full-stack development—from backend services and APIs to cloud-native infrastructure on Kubernetes—with emphasis on enterprise search, retrieval optimization, and AI application integration. Must have strong programming skills across Python, Java, C#/.NET, Rust, TypeScript/JavaScript, and proven experience architecting and operating scalable, production-ready AI systems.

Lead the design and implementation of AI-assisted HR systems at ServiceNow, owning agentic workflows, evaluation infrastructure, and production safety mechanisms for large-language-model-powered employee-relations features. You will author and refine prompts and agent instructions, build multi-turn evaluation suites with drift detection and CI gating, and ensure entitlement-aware data handling across retrieval and tool layers.…See more Collaborate across product and engineering to translate business requirements into robust technical designs, and drive hands-on integration, migration, and incident resolution on customer-facing systems. Requires 10+ years of software engineering with direct production LLM experience, orchestration migrations, system design rigor, and proficiency in Java, JavaScript/TypeScript, and React.

Staff Machine Learning Engineer to drive AI/ML-based automation across Qualcomm's Automotive SoC Cybersecurity and Safety engineering. The role focuses on building intelligent, scalable solutions using LLMs, GenAI, and agentic AI systems to streamline cybersecurity workflows, functional safety processes, and silicon verification.…See more Responsibilities include designing end-to-end ML pipelines, developing document-understanding and review-automation systems, and applying AI to security analysis and compliance monitoring across the product development lifecycle. Required: strong ML/AI background, Python or C++ proficiency, production ML experience; preferred: knowledge of automotive standards (ISO/SAE 21434, ISO 26262), hardware security, or RTL/verification workflows.

Build and deploy production-ready AI solutions using LLM orchestration platforms (LangChain, LangGraph, CrewAI, n8n) and agentic workflows that integrate with existing data infrastructure like Snowflake. Develop sophisticated prompt engineering and RAG systems with vector databases, conduct independent data analysis to identify AI opportunities, and translate technical work into clear insights for leadership.…See more Move solutions from concept to deployed, maintainable tools with proper documentation and intuitive interfaces. Collaborate cross-functionally with data scientists, analysts, and stakeholders to drive organizational impact.

As a Forward Deployed Engineer IV in Applied AI at Google Cloud, you will lead end-to-end engineering for production conversational AI systems, transforming prototypes into scalable agentic workflows deployed at customer sites. You'll architect multi-agent systems using frameworks like ReAct, optimize RAG implementations, debug agent logic across microservices, and connect AI systems to enterprise infrastructure.…See more The role requires 8+ years of software development (Python or similar), experience with infrastructure-as-code (Terraform), cloud platform deployment (GCP), and hands-on full-stack application development. You'll travel up to 50% and work directly with customers to establish production AI journeys while identifying patterns that feed back into product improvements.

Design and build AI agents that automate workflows within a multi-agent platform, enabling PM and engineering agents to exchange plans and context over shared program data. Develop full-stack web applications with generative AI functionality—conversational interfaces, natural-language querying, automated summarization, and in-context recommendations—using C#, .NET, Python, TypeScript, Angular/React, and integrations with Jira, Microsoft 365, Confluence, and SQL/NoSQL stores.…See more Define agentic engineering standards including tool contracts, MCP servers, prompt management, memory, guardrails, human-in-the-loop approval, and evaluation harnesses. Requires hands-on experience with LLM application development, agentic runtimes like LangGraph or AutoGen, full-stack enterprise development, and cloud deployment on AWS or Azure.

This role combines prompt engineering expertise with AI system governance and enterprise adoption strategy. The engineer will optimize LLM performance across platforms (OpenAI, Anthropic), design and configure AI agents and chatbots with knowledge grounding, and establish prompt governance frameworks and best practices.…See more Success requires 5+ years in AI/ML systems, 2+ years of hands-on prompt engineering, and demonstrated ability to evaluate AI performance, manage knowledge systems, and guide enterprise clients through AI tool adoption and deployment.

Mid-level full-stack software engineer responsible for developing, deploying, and troubleshooting distributed applications across application, OS, networking, and hardware layers. Must have 5+ years of professional experience with Python, modern front-end frameworks, RESTful APIs, Linux (RHEL preferred), containerization (Podman/OCI), Ansible automation, and Git.…See more Role emphasizes independent ownership of features from requirements through testing, strong debugging across system and software components, and integration with external systems and hardware interfaces.

As a Software Architect, you will design AI-first software development lifecycles and lead teams in building GenAI-integrated platforms. You'll architect solutions using prompt engineering, RAG, embeddings, and agentic AI while managing multi-developer environments where AI agents and engineers collaborate.…See more You'll own product architecture, prototype AI-driven solutions with modern DevOps and observability, and coach teams on AI-accelerated development practices. The role requires 6+ years of production software experience, hands-on use of agentic IDEs, proven GenAI integration delivery, and strong architecture expertise across APIs, microservices, cloud platforms, and CI/CD.

Principal AI Platform Engineer leading enterprise AI infrastructure strategy and governance at the platform boundary. Responsible for implementing and maintaining Bedrock Guardrails and Microsoft Purview DLP controls, managing AI vendor relationships with Microsoft and AWS, producing governance artifacts and runbooks, and leading internal AI adoption through community building and enablement.…See more Requires 7–10 years of cloud/DevOps experience with hands-on production expertise in AWS Bedrock (Agents, Knowledge Bases, Guardrails) and Microsoft 365 Copilot deployment in enterprise environments, including data integration, RAG architecture, and AI security controls.