model-context-protocol jobs in San Diego

The AI Scientific Software Engineer will develop MARIE, an AI-powered scientific companion within BIOVIA's Pipeline Pilot platform, focusing on prompt engineering, agentic AI frameworks, and AI safety mechanisms. Responsibilities include designing and iterating on prompts for improved reasoning and factuality, building multi-step agentic workflows with tool calling and memory management, and implementing guardrails and validation systems.…See more The role requires a PhD in Life Sciences or Computer Science with 3+ years of software development experience, strong LLM fundamentals knowledge, hands-on experience with RAG systems and frameworks like LangChain/LangGraph, and familiarity with pharmaceutical and biotech workflows.

You will develop AI-powered scientific software for MARIE, an AI companion within BIOVIA's Pipeline Pilot platform serving pharmaceutical and biotech researchers. The role focuses on prompt engineering, agentic AI frameworks, and AI safety mechanisms—designing and optimizing LLM-based workflows for multi-step scientific tasks.…See more You'll implement RAG systems, semantic search, vector databases, and guardrails while collaborating across engineering, QA, product, and technical teams using Agile practices. The position requires a PhD in Life Sciences or Computer Science, 3+ years of software development, and hands-on experience with LLMs, agentic frameworks like LangChain, and prompt optimization.

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.

Lead security operations and threat detection engineering for enterprise clients using Google SecOps (Chronicle SIEM and Siemplify SOAR). Design and deploy log ingestion pipelines, develop threat detection rules, translate SOC processes into automated playbooks, and manage integrations across security platforms.…See more Mentor junior SOC engineers and collaborate with threat detection teams to reduce alert fatigue and scale response capabilities.

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.

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.

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 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.

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.

Lead the design, governance, and technical implementation of an enterprise API and AI gateway platform serving internal and external consumers. Define solution standards, security controls, compliance protocols aligned to financial services regulations, and LLM integration patterns across Apigee X, MCP servers, and multiple AI providers.…See more Manage a team through API modernization and AI gateway buildout while actively contributing hands-on engineering on proxies, policies, and MCP integrations when needed.

AI Architect role requires designing and delivering enterprise-scale AI/ML solutions on AWS, with 5–7 years of cloud experience and 2–3 years hands-on with LLMs, agentic frameworks, and AI/ML services. The position demands deep expertise in AWS AI services (Bedrock, SageMaker, Kendra), orchestration frameworks (LangChain, LangGraph), RAG pipelines, MLOps tooling, and responsible AI principles.…See more Candidates must architect scalable, cost-optimized solutions integrating AI with cloud infrastructure, data platforms, and microservices, and communicate complex technical concepts to executive and technical audiences.

Lead Qualcomm's GenAI transformation for embedded and software engineering, building reusable AI agents, agentic workflows, and developer productivity tools that scale across teams. Partner with platform and engineering teams to establish secure AI practices, governance models, and metrics while serving as a technical thought leader who shapes the organization's transition to AI-native engineering workflows.…See more Requires Principal-level technical leadership with deep expertise in generative AI, LLM-based systems, and hands-on experience deploying GenAI solutions in complex engineering environments. Collaborate on code generation, code review, test automation, and release readiness use cases while driving measurable productivity and quality improvements.

Design and build production AI solutions including copilots, agents, RAG applications, and workflow automations on Azure OpenAI, Claude, and Microsoft platforms. Write custom code in Python, JavaScript/TypeScript, or C# and leverage low-code platforms like Power Automate and Logic Apps where appropriate, following solid engineering practices (version control, testing, CI/CD, AI output evaluation).…See more Integrate solutions with enterprise systems and APIs with security-first design, own projects end-to-end from deployment through adoption and improvement, and work with security, architecture, governance, and compliance teams to ensure production readiness. Communicate clearly to both technical and business audiences, document designs, and coach teammates on AI capabilities, limits, and risks.

Senior Applied AI Engineer at West Health will design, build, and deploy production-quality AI-powered tools and workflows using large language models, multi-agent architectures, and orchestration platforms. The role involves building intelligent automation pipelines with tools like n8n, LangChain/LangGraph, and Microsoft Copilot Studio, integrating AI capabilities with existing data infrastructure (Snowflake, Python), and conducting data analysis to identify high-impact AI opportunities.…See more The ideal candidate combines deep practical expertise in applied AI with the ability to rapidly move from concept to fully working, well-documented applications that create measurable value for healthcare delivery and senior care initiatives.