agents jobs in San Diego

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.

Senior Associate AI Engineer will develop GenAI/LLM applications and integrations using foundational models, build and test AI models and conversational agents, and configure AI solutions into enterprise applications on cloud platforms. The role requires assisting with data preparation for model training, documenting technical workflows, and staying current on emerging AI tools.…See more Requires minimum three years of professional or academic experience in AI/ML, data science, or software engineering, proficiency in Python or similar languages, and hands-on experience with a major cloud AI platform (Azure AI, AWS Bedrock, or Google Cloud Vertex AI).

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.

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.