tool-calling jobs in San Diego

As an AI Scientific Software Engineer, you will develop MARIE, an AI-powered scientific companion within the BIOVIA 3DEXPERIENCE platform for pharmaceutical, biotech, and fine-chemical industries. You will build knowledge integration systems, agentic AI frameworks with tool calling and reasoning loops, and implement AI safety guardrails and validation mechanisms.…See more The role requires strong Python and LLM expertise, hands-on experience with frameworks like LangChain and LangGraph, RAG systems, prompt engineering, and vector databases. You will work collaboratively across application development, QA, product, and technical teams using Agile practices.

As an AI Scientific Software Engineer, you will develop MARIE, an AI-powered scientific companion within the 3DEXPERIENCE platform, focusing on knowledge integration, agentic AI workflows, and AI safety. You'll build systems that connect scientific data sources, implement retrieval-augmented generation (RAG) and semantic search capabilities, orchestrate multi-step agentic workflows with tool calling and reasoning loops, and establish guardrails for AI reliability.…See more The role requires strong Python expertise, deep LLM fundamentals (tokens, temperature, context windows), hands-on experience with frameworks like LangChain and LangGraph, and the ability to design and optimize prompts for scientific reasoning tasks.

Design and architect enterprise-grade AI applications on Google Cloud, with deep expertise in LLMs, Gemini, and Vertex AI. Lead end-to-end solution design across data pipelines, model lifecycle, cloud infrastructure, and MLOps/LLMOps.…See more Implement RAG and agentic solutions, enforce security and governance for AI/ML systems, and guide cloud-native development on GKE, Cloud Run, and related Google Cloud services. Requires 6+ years as a software/solutions architect, 5+ years hands-on GCP, 2+ years production Vertex AI/Gemini experience, and a hyperscaler architect certification.

Staff Engineer role focused on architecting and developing full-stack applications with AI-native development at its core. Responsibilities include designing RESTful APIs, managing databases, and critically—directing AI coding agents (Claude, Cursor, Copilot) across the delivery lifecycle, including prompt engineering, agent workflow design, and tracing/debugging agent outputs.…See more Requires 8+ years with complex codebases, full-stack expertise (Java/Go/Python, React, REST APIs), and demonstrated proficiency using AI coding tools as a primary delivery vehicle, not just a helper.

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.

Advanced manager and technical consultant designing and implementing enterprise-scale AI solutions spanning classical ML, generative AI, and agentic AI systems. Responsibilities include architecting AI-powered applications for business transformation (customer service, supply chain, finance, HR, operations), building RAG and autonomous agent workflows, developing ML pipelines, and establishing governance and observability frameworks.…See more Requires 8+ years in AI/ML/data science with 5+ years hands-on experience with Claude, GPT, LLMs, agentic frameworks, and RAG systems, plus deep expertise in prompt engineering, vector databases, and multi-agent architectures.

Lead a team of consultants building production AI/ML applications for cybersecurity, with hands-on responsibility for architecture, code review, and integration across SIEM, SOAR, EDR, and other security platforms. Own technical delivery, timelines, and budgets while mentoring engineers and staying engaged in pair programming and troubleshooting.…See more Work with frontier LLM providers to evaluate agentic systems, RAG pipelines, tool-calling agents, and evaluation harnesses for threat detection and incident response automation. Partner with client stakeholders to translate cyber capabilities into engineering requirements and support account growth through proposals, demos, and reusable delivery playbooks.

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.

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.

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.

Senior AI Software Engineer responsible for designing, developing, and deploying LLM-powered applications and AI-assisted development workflows within a large-scale enterprise Linux environment (~6M+ LOC, primarily C/C++). Build high-performance GPU-based inference pipelines using vLLM and modern frameworks, develop agentic AI workflows with RAG and tool calling, and integrate LLMs with vector databases and enterprise systems into production.…See more Collaborate across software engineers, AI researchers, and platform teams to productionize AI services while optimizing for performance, latency, scalability, and operational efficiency. Requires 4+ years software development, strong Linux/Red Hat experience, advanced C/C++, Python, LLM/RAG expertise, and GitLab CI/CD workflows.

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, build, and ship LLM-powered capabilities end to end—from prototyping and fine-tuning models to deploying production agents and retrieval systems. Own the full stack: prompt and context engineering, multi-step agent design with tool calling, RAG systems (embeddings, chunking, hybrid search, reranking), fine-tuning on multi-GPU with LoRA/QLoRA, evaluation and tracing infrastructure, and clean APIs for other engineers.…See more Work in a secure, distributed environment where the platform runs on customer compute, cloud, or hybrid setups, requiring expertise with both commercial and self-hosted models.

Build the foundational agentic AI layer for a materials-science platform, including multi-model provider abstraction, agent orchestration with stateful checkpoints, retrieval systems, prompt versioning, and comprehensive tracing and evaluation frameworks. You'll design agents that plan and reason over tool calls in production, implement human-in-the-loop safety gates, and ensure all LLM behavior remains auditable and cost-tracked across customers' secure environments.…See more The role demands deep production experience with agentic and LLM systems: async Python, structured outputs, memory and context management, multi-step workflow orchestration, and evaluation harnesses that catch regressions before deployment.