AI Engineer II

Quick Facts

• Role: AI Engineer II (hands-on AI/ML builder)

• Focus: AI agents, LLMOps, RAG pipelines, model deployment, and operational readiness

• Work pattern: Hybrid; must live within commutable distance to Memphis, TN; Plano, TX; or Pittsburgh, PA

Description

Build and operationalize AI/ML solutions for intelligent automation, predictive insights, and advanced analytics across the enterprise. Own end-to-end operational readiness by taking AI use cases from prototype to governed, business-ready production, including evaluation automation, observability, SLOs, and incident response/rollback. Collaborate with Data Scientists, Data Engineers, MLOps, and Platform teams to integrate AI services into enterprise applications.

Responsibilities

• Deploy and operationalize ADK agents using Vertex AI Agent Engine, Cloud Run, and GKE

• Implement LLMOps workflows: automated agent evaluations, continuous regression testing, and production observability (telemetry, tracing, latency monitoring)

• Design and implement ML/optimization/Operations Research solutions using production-quality code

• Build preprocessing, feature engineering, and model training workflows

• Optimize deployed AI models for performance, scalability, reliability, and maintainability

• Implement high-performance RAG architectures with Vertex AI Vector Search, BigQuery Vector Search, and GCS

• Create ingestion, chunking, and embedding pipelines using Apache Beam and Cloud Run Jobs

• Integrate enterprise datastores (BigQuery, Cloud SQL/AlloyDB pgvector, Firestore) as tool-accessible repositories

• Architect multi-agent systems and workflow-based agents in Python using Google ADK

• Use Google Agent CLI to scaffold, run, test, evaluate, and package agents across environments

• Implement agentic patterns (graph workflows, hierarchical delegation, HITL tool confirmations, session/state memory)

• Build custom tools and integrate MCP and foundation-model-powered API connectors on Vertex AI

• Partner with teams to integrate AI endpoints into enterprise workflows and maintain documentation for transparency and adoption

• Ensure compliance with enterprise governance, security, and Responsible AI standards

• Evaluate emerging generative AI/LLM technologies for business value

Benefits

• Health, vision, and dental insurance

• Retirement plans

• Tuition reimbursement