vector-databases jobs in San Diego

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.

Design and build production AI systems integrated into enterprise workflows, owning the full stack from APIs and backend services through cloud infrastructure, data pipelines, and AI components. Lead multidisciplinary engineering teams, make architectural decisions across cloud platforms (AWS, Azure, GCP), containers, serverless, and infrastructure-as-code, while building agentic systems with tools, memory, and safety controls.…See more Own DevSecOps practices including CI/CD automation, security scanning, and incident response. Require 5+ years of production software engineering, full-stack development across multiple layers, hands-on experience with a major cloud platform, and proficiency in languages like Java, Python, Go, TypeScript, or C#.

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.

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.

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.

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.

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.

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.

Lead enterprise AI platform strategy and execution at a biotech company, driving end-to-end program delivery across engineering, data, cybersecurity, and product teams. Own platform health, KPIs, and telemetry; establish governance, safety guardrails, and regulatory compliance for AI-enabled solutions in a regulated environment.…See more Define multi-year AI roadmaps, coordinate cross-functional dependencies, and orchestrate Agile delivery while partnering with legal, compliance, and tech enablement to ensure scalability, security, and enterprise adoption.

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.

Senior Data Architect with 15+ years of experience designing and implementing enterprise data platforms that support modern AI/ML and Generative AI applications. Must have strong hands-on expertise in ETL/ELT, Python, SQL, and enterprise data warehousing, with production experience in Treasure Data, real-time analytics, streaming pipelines (Apache Kafka), and AI-ready data architectures including RAG, vector databases, and LLM integrations (LangChain, LangGraph, Azure OpenAI, AWS Bedrock).…See more Will design batch and real-time enterprise data architectures on cloud platforms (AWS, Azure, GCP, Snowflake, Databricks) with CDC/Debezium incremental processing capabilities.

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.

This Staff AI Engineer role focuses on architecting and implementing secure, scalable frameworks for autonomous AI agents at enterprise scale. The engineer will lead the technical roadmap for agent management—from provisioning and orchestration to compliance and retirement—while ensuring AI agents are secure by design with built-in visibility and guardrails.…See more The role requires 8+ years of software architecture and backend systems experience, deep knowledge of autonomous agent frameworks (LangChain, AutoGen, CrewAI), and expertise in cloud infrastructure (AWS, Azure, GCP) and Kubernetes. You'll collaborate cross-functionally with researchers, platform engineers, and product teams to bring agentic AI capabilities to production while establishing best practices for agent safety, observability, and ethical deployment.

Design, build, test, and operate production ML and LLM-powered components for cybersecurity applications at scale. You will turn ambiguous requirements into working code, partner across product and security teams, and apply AI safety and guardrail practices.…See more The role requires solid software engineering fundamentals, hands-on experience building production ML/LLM applications (RAG, embeddings, agents), and the ability to take prototypes to reliable, maintainable systems. You'll work with distributed systems, cloud-native technologies, and graph/data plumbing while contributing to code reviews and raising team quality standards.

The AI Engineer designs, builds, and operates production-grade AI solutions in Azure Government (GCCH) environments, with a focus on LLM and RAG implementations. Responsibilities include testing and optimizing LLMs and model variants, benchmarking RAG systems, building MCP server integrations to connect AI agents to enterprise data, implementing agentic AI workflows using platforms like N8N and Copilot Studio, and developing modular AI microservices.…See more The role also involves supporting security practices (RBAC, Key Vault, data protection), monitoring and incident response for AI workloads, and helping BI and DevOps teams adopt AI capabilities toward production. A Bachelor's degree in Computer Science or related field and 1–2 years' experience with LLMs, RAG, or AI solution development are required.

The AI Engineer designs, builds, and operates production-grade AI solutions in Azure Government environments, focusing on LLM and RAG implementations, agentic AI workflows, and secure enterprise integrations. Responsibilities include testing and optimizing language models, benchmarking RAG systems, building MCP server integrations, implementing multi-step AI agents using platforms like N8N and Copilot Studio, and developing modular AI microservices.…See more The role requires hands-on support for security (RBAC, Key Vault, data protection), monitoring, and incident response in Azure Gov, with a focus on moving proofs of concept toward production deployment.

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.

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.

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, 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.