Staff AI Engineer
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Job Description
Staff AI Engineer developing innovative AI solutions for healthcare enterprise using modern AI systems and frameworks. Collaborating with cross-functional teams to implement and monitor AI systems at scale.
Responsibilities:
- Architect and develop enterprise-scale multi-agent systems leveraging LLMs and autonomous agent frameworks using Google ADK, Agentspace, MCP, RAG, and A2A orchestration
- Design and implement RAG pipelines using BigQuery and Vertex AI Engine for knowledge grounding and factually accurate responses
- Optimize agents for orchestration, knowledge grounding, multi-step reasoning, and decision-making
- Design and implement distributed training workflows, online inference systems, and low latency serving architectures optimized for real-world performance, using Google cloud-native services
- Engineer scalable, secure, compliant and production-grade AI fabric and AI agent workflows using Vertex AI and modern cloud-native technologies
- Create reusable agent orchestration layers, observability hooks, and governance frameworks that accelerate Agentic AI adoption across TAG brands
- Partner with cross-functional stakeholders in translating business requirements into technical specifications
- Own the full AI development lifecycle – from data collection and implementation to deployment and monitoring
- Implement intelligent observability and automation strategies to ensure AI system reliability and performance at scale
Requirements:
- BS in Computer Science, or related technology field or equivalent experience
- 2+ years of experience in Agentic AI engineering
- 4+ years of experience in AI/ML engineering
- 8+ years of experience in software engineering, or platform engineering
- Proven track record of building and deploying production-grade AI/ML systems at scale
- Deep understanding of modern AI model architectures (e.g., transformers, diffusion models) and system design
- Strong hands-on expertise with Vertex AI (including model training, pipelines, orchestration, deployment, and monitoring) and Google’s Agentic AI stack
- Hands-on with one or more of these agent orchestration frameworks: Google ADK/Agentspace, LangChain, LangGraph, LlamaIndex, CrewAI or AutoGen
- Proficiency in Python, LLM integration workflows, MCP (Model Context Protocol) for tool integration and A2A (Agent-to-Agent) orchestration for multi-agent workflows
- Expertise in distributed training, online inference, and low latency serving architectures
- Experience with Kubernetes, Cloud Run, and Dataflow/PubSub for scalable deployment
Benefits:
- paid time off
- health insurance
- dental insurance
- vision insurance
- 401(k) savings plan with match
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