rag-retrieval-augmented-generation jobs in San Diego

Lead Forward Deployed Engineer responsible for architecting and delivering LLM-enabled applications (copilots, agentic workflows, assistants) using enterprise AI platforms like Claude, Gemini, or GPT-4o. Drive end-to-end RAG pipeline design, prompt engineering, evaluation frameworks, and data foundations while mentoring junior engineers and enforcing delivery standards across multi-pod engagements.…See more Require 7+ years software/data engineering experience, 1+ year hands-on GenAI/LLM production deployment, and proven ability to lead project workstreams and translate business problems into AI solutions.

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.

Join XiFin's Advanced Data Analytics team as an Associate Machine Learning Engineer to design, develop, and deploy machine learning models with a focus on LLMs, Generative AI, and Agentic AI in healthcare and revenue cycle domains. You will contribute to MLOps pipelines, perform data preparation and prompt engineering, collaborate with cross-functional teams, and validate models for production use.…See more The role requires a Bachelor's degree in Computer Science, Data Science, or related field, with proficiency in Python, ML libraries (scikit-learn, TensorFlow, PyTorch), and foundational knowledge of GenAI architectures, vector databases, and LLM orchestration frameworks.

This is a hands-on Solution Architect role for an AI-enabled Managed Services team, requiring deep technical expertise in designing end-to-end application architectures spanning UI, APIs, services, data, and AI components. The role combines hands-on coding and prototyping with architecture ownership, emphasizing active participation in codebases, AI tool evaluation, and guiding engineering teams through complex technical decisions.…See more Key responsibilities include designing reference architectures for AI patterns (RAG, vector search, prompt orchestration), defining non-functional requirements (performance, reliability, security), architecting CI/CD pipelines, and ensuring production readiness. The ideal candidate has 8+ years of software engineering experience, strong cloud-native and modern application architecture knowledge, practical AI feature development, and comfort working directly in code and infrastructure.

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.

This role blends data science expertise with hands-on automation engineering to design and deploy intelligent process automation solutions across business units. You will build, train, and validate both classical ML and LLM-based models — including fine-tuning and RAG — and architect end-to-end systems connecting CRM, APIs, and AI services on AWS infrastructure.…See more Responsibilities span solution design, CI/CD implementation with Jenkins, Python development, agentic AI capabilities, and business translation, with emphasis on measurable business outcomes and compliance.

Design, build, and operate production-quality agentic AI systems and developer tooling at scale. Lead multi-agent orchestration, Model Context Protocol (MCP) integration, prompt engineering for structured output, and CI/CD automation for code review and testing.…See more Partner with engineering teams on discovery, rapidly prototype and validate capabilities, then own the full lifecycle from implementation through production monitoring. Mentor engineers on agentic design patterns and communicate technical tradeoffs to stakeholders.

Build production AI features and workflows powered by large language models, including retrieval-augmented generation (RAG), agentic orchestration, and tool/function calling integrations. You will design and implement scalable microservices in Python or Java that connect foundation model APIs to business data and internal systems, with responsibility for AI quality, evaluation, observability, and safe deployment on AWS.…See more Collaborate with cross-functional teams to prototype solutions, measure outcomes, and move proven capabilities into production while maintaining reliability, security, and auditability.

Senior Software Engineer responsible for designing and communicating architectural decisions across technical and non-technical teams at a healthcare technology company. Will reduce technical debt, improve developer experience, provide code and design reviews, and mentor engineers in AI-enabled development practices.…See more Requires 8+ years of software engineering experience with strong fundamentals in production systems, APIs, distributed systems, cloud-native applications, CI/CD, and observability. Should demonstrate leadership in technical initiatives, influence without formal authority, and hands-on proficiency with AI-assisted development tools while maintaining code quality accountability.

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.

Design, develop, and optimize production machine learning systems including model development, inference optimization, and scalable ML infrastructure. Build end-to-end ML pipelines from training through deployment, optimize model inference for latency and cost, integrate LLM/ML models into APIs and microservices, and engineer data pipelines for preprocessing and validation.…See more Requires strong software engineering fundamentals with deep ML expertise, proficiency in Python and systems languages, and experience with ML frameworks, model serving, and distributed computing.

Senior Staff Software Engineer responsible for architecting and leading Intuit's Virtual Expert Platform (VEP), an agentic AI customer success system that serves Customer Success Managers, onboarding specialists, and partner teams. The role requires deep hands-on development (50–70%) alongside strategic architecture work, designing multi-agent systems end-to-end, establishing technical standards and AI safety frameworks, and driving complex cross-team technical problems to resolution.…See more This engineer will define observability and guardrail patterns for AI in production, mentor senior engineers, and influence product roadmap strategy with senior business leaders.

Lead end-to-end AI platform architecture and infrastructure at enterprise scale, designing and implementing generative AI capabilities including LLMs, RAG systems, and agentic AI with a focus on model serving, inference optimization, and production deployment. Establish MLOps/LLMOps best practices, build automated CI/CD pipelines tailored for AI/ML applications, and architect vector database integration and cloud AI/ML services.…See more Mentor engineering teams, drive cross-functional collaboration between software, product, and data teams, and continuously evaluate and optimize AI platform performance, scalability, and reliability.

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 technical leader responsible for integrating AI/ML systems into secure, scalable DoD-compliant environments. Will architect AI system deployments, establish DevSecOps practices, ensure compliance with RMF/NIST 800-53/DISA STIG standards, and provide technical guidance across engineering teams.…See more Requires deep expertise in AI/ML production systems, containerization, CI/CD, and cybersecurity controls, with hands-on experience in Python, Kubernetes, and modern infrastructure tooling.

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.

As Product Success Technical Director for AI Adoption Engineering at ServiceNow, you will own escalation resolution and architecture consulting for the company's most complex agentic AI deployments. You'll diagnose and stabilize at-risk customer go-lives, design resilient agentic architectures (LLM integration, RAG, multi-agent orchestration, guardrails, governance), and serve as the technical authority for customer executives during high-stakes moments.…See more You'll mentor the team, codify reusable playbooks and reference architectures, and act as a strategic conduit between field escalations and Product/Engineering to turn hard-won patterns into scalable capability.

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.

The AI SW Integration Engineer will develop backend tools and services that automate test generation, improve requirements traceability, and streamline engineering workflows across teams. The role requires strong expertise in generative AI and large language models (LLMs), with hands-on experience designing and deploying agentic AI systems end-to-end.…See more You will integrate tools and databases via REST APIs, parse structured documents, and build automation solutions using Python and CI/CD pipelines. The ideal candidate brings 3–5 years of production experience developing automation or integration tools, plus familiarity with RAG, MCP, and requirements management workflows.

Design, implement, and troubleshoot AI/LLM-enabled software solutions for Navy cyber missions, including scripts, services, APIs, and automation workflows. Integrate systems across REST/HTTP APIs, relational and NoSQL data stores, and cloud or enterprise environments while respecting RMF controls and secure engineering practices.…See more Translate mission and security requirements into concrete technical tasks, deliver hands-on code in Python, and communicate trade-offs to both technical and non-technical stakeholders. Work in small Agile teams using Git and code review practices, with mentorship to reduce cyber risk across fleet and ashore enterprise.

Senior Machine Learning Engineer to design and ship production voice and conversational AI agents within AppFolio's Realm-X platform. You will architect real-time, multi-turn agent pipelines that balance reasoning depth against latency, lead a small pod of ML and platform engineers, and define quality metrics and evaluation harnesses.…See more Required expertise includes shipped production experience with agent frameworks (LangChain, LangGraph), voice stacks (STT/TTS/Voice-to-Voice models), LLM reasoning and tool use, Twilio, AWS, expert Python with async and WebSocket streaming, and demonstrated team leadership.

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.