rag-retrieval-augmented-generation jobs in San Diego

Staff Engineer or Architect role focused on building and orchestrating AI agents in a compliance-heavy, globally regulated environment. You will troubleshoot novel failures across models, tools, and infrastructure; set technical vision; and leverage LLMs, RAG, tool use, and agent frameworks (LangGraph, CrewAI, AutoGen) in production systems.…See more Requires 8+ years of software engineering experience, hands-on LLM and agent-framework work, end-to-end ownership, and demonstrated ability to use AI agents effectively in your own workflow while maintaining accountability and clear mental models of system behavior.

The Enterprise AI FinOps Lead establishes financial governance for company-wide AI usage across multi-cloud (Azure, AWS, GCP), SaaS, and on-premise environments. The role owns cost visibility, show-back, optimization, budgeting, forecasting, and stakeholder engagement, with deep responsibility for AI-specific cost drivers such as tokens, models, prompts, embeddings, and RAG systems.…See more The leader translates telemetry into actionable insights, optimization programs, and executive reporting while managing the full departmental budget. This is a hands-on operator role requiring 8+ years in FinOps, cloud cost management, or enterprise finance, with strong analytical, stakeholder management, and executive communication skills.

Qualcomm seeks a Machine Learning Engineer to design, develop, and deploy generative AI solutions focused on Large Language Models, Retrieval-Augmented Generation (RAG), and intelligent agent systems. The role involves implementing RAG-based solutions, optimizing LLM fine-tuning strategies, building evaluation frameworks, and maintaining agentic workflows for autonomous AI systems.…See more The candidate must have strong Python programming skills, practical LLM deployment experience, and familiarity with ML frameworks like PyTorch and TensorFlow, alongside knowledge of vector databases, cloud platforms (AWS, GCP, Azure), and modern MLOps infrastructure.

Lead engineering and data science roadmap for Teradata's AI Platform, overseeing architecture, LLM integrations, RAG pipelines, vector store implementations, and data science experimentation frameworks. Partner with product, research, and engineering teams to define requirements for predictive modeling, multi-agent collaboration, and tool orchestration.…See more Manage and mentor a team of backend engineers, data scientists, and AI platform specialists while driving technical excellence, cloud-native practices, and platform scalability.

Senior Full-Stack Software Engineer responsible for designing and maintaining end-to-end web applications powering Clairyon's clinical AI platform. The role requires building production-grade machine learning integrations, secure APIs for real-time clinical and sensor data ingestion, and optimized data pipelines and model-serving infrastructure.…See more The engineer will collaborate with data scientists, clinicians, and product teams to translate research models into scalable software solutions, with daily tasks including code reviews, testing, documentation, and architectural contributions. Required expertise spans full-stack development (Python, TypeScript, JavaScript), cloud platforms (AWS), containerization (Docker, Kubernetes), AI agents and RAG architectures (LangChain, DSPy), healthcare data standards (HL7 FHIR), and healthcare compliance (HIPAA).

AppFolio is hiring a Staff Machine Learning Engineer to design, build, and operate their ML platform on AWS, supporting training, fine-tuning, inference, RAG, and cost optimization across the organization's AI initiatives. You'll partner with applied AI and research teams to productionize prototypes, maintain multi-provider LLM reliability (OpenAI, Google, Anthropic), and operate AI safety guardrails and authorization layers.…See more The role requires production-scale ML infrastructure experience on AWS (ECS, SageMaker, GPU fleets), deep knowledge of model serving and inference optimization, hands-on language model training, and demonstrated cost discipline across AI workloads.

Senior AI Engineer building AI-powered learning systems for Shield AI's Hivemind autonomy platform. The role involves developing an AI training coach for context-aware guidance, creating tooling to generate technical workshops and curricula from learning objectives, and partnering with autonomy experts to produce assessments and labs.…See more Success is measured by shipped learning products, reduced time to technical proficiency, and demonstrated improvement in customer capability and product adoption.

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.

Chief AI Subject Matter Expert leading the development of intelligent data processing pipelines, NLP systems, and AI-enabled dashboards for defense applications. The role requires 15+ years of experience with generative AI, LLMs, and agent-based development workflows, including hands-on use of tools like GitHub Copilot and Claude for code generation and automation.…See more Responsibilities include prototyping emerging AI tooling, mentoring engineering teams on AI integration, designing secure production AI solutions, and identifying opportunities to accelerate the software lifecycle through AI and automation. Must demonstrate expertise in LLM APIs, RAG architectures, vector databases, prompt engineering, and AI-specific security considerations in classified/sensitive environments.

Design and build AI agents and full-stack web applications that automate enterprise workflows, with expertise in agentic AI frameworks, LLM orchestration, and generative AI integration. Architect a multi-agent platform enabling PM and engineering agents to exchange plans and context, while developing backend services in C#/.NET/Python/TypeScript and modern Angular/React frontends that connect to enterprise systems like Jira, Microsoft 365, and SQL/NoSQL databases.…See more Define agentic engineering standards including tool contracts, prompt management, guardrails, and agent evaluation. Requires hands-on experience with LLM application development, RAG, tool calling, prompt engineering, agentic runtimes (LangGraph, AutoGen, n8n), and full-stack enterprise application development on AWS or Azure.

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.

This Technical Lead role focuses on architecting, developing, and deploying production-grade Agentic AI solutions on AWS using .NET/C#. The ideal candidate will have hands-on expertise in LLMs, generative AI, RAG, prompt engineering, and agent frameworks (LangChain, LangGraph, Semantic Kernel, AutoGen), combined with strong .NET enterprise development and AWS cloud-native skills.…See more Responsibilities include taking AI solutions from POC to production, designing microservices and REST APIs, and ensuring AI evaluation, monitoring, observability, and governance. Technical leadership, stakeholder management, and system design fundamentals are essential.

Sr Manager leading a team of Platform Engineers/DataOps analysts to architect, deploy, and manage enterprise-scale Databricks Unity Catalog across dev/test/production environments. Responsibilities span platform governance, fine-grained access control (RBAC/ABAC), workspace automation via Terraform, CI/CD pipeline design (GitHub Actions, Azure DevOps, GitLab CI), FinOps optimization, and incident management for critical data infrastructure.…See more Requires 10+ years in data operations/platform engineering with 5+ years as a lead Databricks administrator, deep expertise in Unity Catalog governance, cloud security (IAM, VPC/VNet, compliance), and proficiency in Python, PySpark, and SQL.

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.

Design and architect AI-powered engineering productivity solutions and agentic workflows to automate software development lifecycle processes at scale. Build automation platforms, engineering dashboards, and AI-assisted debugging/triage systems while collaborating with engineering and platform teams.…See more Drive adoption of generative AI and LLMs within software engineering workflows, mentor engineers, and define technical direction for productivity services. Requires strong system design skills and proficiency in Python or C++, with preferred experience in LLMs, RAG systems, CI/CD, and internal developer platforms.

Build practical generative AI applications as a Python backend engineer, combining LLMs, RAG, and agentic AI systems. You'll develop FastAPI/Flask services, integrate LangChain/LangGraph workflows, manage vector databases, and deploy on AWS.…See more The role requires hands-on Python backend skills, prompt engineering, API design, and the ability to debug and own full-stack AI-powered services from development through testing and deployment.

Design and implement AI-powered engineering productivity solutions, including agentic workflows and automation platforms for large-scale software development organizations. Develop frameworks for AI-assisted debugging, triage, and root cause analysis while building engineering dashboards and operational metrics.…See more Architect and scale engineering automation across the software development lifecycle, partnering with platform and product teams to identify high-value opportunities. Mentor engineers and drive adoption of generative AI and LLM technologies within enterprise software engineering workflows.

Design, build, and maintain secure enterprise data platforms supporting AI/ML workloads, with responsibility for database optimization, CI/CD automation, and DoD compliance. Mentor junior engineers and provide technical leadership on architecture decisions.…See more Implement security controls aligned with NIST 800-53, RMF, and DISA STIG standards throughout the platform lifecycle. Develop ETL/ELT pipelines, support data lakes and lakehouses, and evaluate emerging data technologies for mission fit.

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.

Principal-level individual contributor role leading AI/ML architecture and engineering strategy at scale. Requires 13+ years across ML, data engineering, and distributed systems, with 3+ years shipping production Generative/Agentic AI systems.…See more Hands-on expertise in RAG, vector databases, LLM optimization, agentic orchestration, and MLOps/LLMOps practice. Will set technical direction, own architecture decisions, drive platform initiatives, and raise engineering standards across the organization without direct people-management responsibility.

As a Staff AI Engineer, you will lead the design and evolution of Teradata's enterprise AI platforms, owning architecture decisions for agentic AI, LLM systems, RAG pipelines, and vector stores. You will operate with high autonomy, partner with product and architecture leadership, and drive technical strategy across multiple engineering teams.…See more The role requires 8+ years building backend services, distributed systems, or data/AI platforms, with strong proficiency in Java, Go, or Python and deep expertise in distributed system design, cloud-native architectures, and production AI systems. You will also mentor senior engineers, establish engineering standards, and serve as a technical escalation point for complex system design and reliability challenges.

Director-level role leading a software engineering team at a life sciences company building multi-omic analysis platforms. Requires hands-on full-stack expertise (React, Go, AWS, Temporal) alongside team management, architecture ownership, and end-to-end product delivery.…See more Must drive regulated software compliance (IEC 62304, ISO 13485, FDA 21 CFR Part 820), design controls, and V&V documentation while prototyping with AI tools and scaling agentic workflows within the team.

Director of Software Engineering leading end-to-end delivery across customer-facing products and internal tooling at a life-sciences company. This role combines hands-on full-stack development (React, Go, AWS, Temporal) with team leadership, architecture ownership, and regulated software compliance (IEC 62304, ISO 13485, FDA 21 CFR Part 820).…See more Candidates should have 10+ years of software engineering with 3+ years managing teams, fluency with modern AI coding assistants, and experience shipping instrumentation, cloud SaaS, and ML/AI features.

A Forward Deployed AI Engineer who builds and deploys production AI/ML and Generative AI applications, particularly agentic workflows and RAG systems integrated with enterprise data and business systems. The role requires 6+ years of software/ML/AI engineering experience, advanced Python proficiency, hands-on expertise with LLMs, modern GenAI frameworks (LangChain, LangGraph, LlamaIndex), and cloud platforms (Azure, AWS, GCP).…See more You'll own solutions from prototype through production, working directly with product and business stakeholders to identify where AI creates value, troubleshoot integration issues, and drive continuous improvement.