Domicile Information This is a hybrid position in Plano, TX (first preference), Memphis, TN, or Pittsburgh, PA. Candidates residing within 50 miles of a FedEx campus will be required to work on-site at a FedEx location several times per week.
SummaryThe AI Engineer is responsible for designing, developing, deploying, and maintaining artificial intelligence and machine learning solutions that support intelligent automation, predictive insight, and advanced analytics across the enterprise. As a hands-on builder, this role applies software engineering principles to write production-quality code, build scalable AI systems, including AI Agents and data pipelines, and integrate AI models into new and existing business applications. The AI Engineer collaborates closely with Data Scientists, Data Engineers, ML Ops Engineers, and Platform teams to bring machine learning models from prototype to production. A critical part of this function is to ensure that AI use cases are transitioned from experimentation into reliable, governed, and business-ready solutions by owning their complete operational readiness. This includes implementing robust observability, defining Service Level Objectives (SLOs), and establishing clear incident response and rollback strategies for all AI services.
Essential FunctionsAgent Development & Orchestration (ADK & Python)- Architect, develop, and maintain multi-agent systems, deterministic workflow agents, and autonomous tool-calling pipelines using the Google Agent Development Kit (ADK) in Python.
- Use the Google Agent CLI (agents-cli / adk) to scaffold, run, test, evaluate, and package agent projects across development, staging, and production environments.
- Implement complex agentic patterns including graph-based workflow runtimes, hierarchical multi-agent delegation, human-in-the-loop (HITL) tool confirmations, and state/session memory management.
- Build custom agent tools, Model Context Protocol (MCP) tool integrations, and API connectors powered by foundation models on Vertex AI.
Retrieval & Semantic Data Pipelines- Design and implement high-performance RAG (Retrieval-Augmented Generation) architectures integrated with ADK using Vertex AI Vector Search, BigQuery Vector Search, and Cloud Storage (GCS).
- Build scalable ingestion, chunking, and embedding pipelines using Python, Cloud Dataflow (Apache Beam), and Cloud Run Jobs.
- Integrate structured enterprise databases (BigQuery, Cloud SQL / AlloyDB pgvector, Firestore) as tool-accessible datastores for autonomous agents.
Agent Deployment & LLMOps- Deploy and operationalize ADK agents seamlessly to Vertex AI Agent Engine (Reasoning Engines), Cloud Run, and Google Kubernetes Engine (GKE) using the Agent CLI and containerized Python runtimes.
- Establish automated evaluation suites using agents-cli eval, Vertex AI GenAI Evaluation Service, and continuous regression testing for reasoning, tool fidelity, and context relevance.
- Implement production-grade agent observability, session telemetry, step-by-step tracing, and latency monitoring using Cloud Trace, Cloud Logging, and Vertex AI Model Monitoring.
- Define and own Service Level Objectives (SLOs) (e.g., TTFT, p50/p95 execution latency, tool invocation error rates, agent task completion rates).
Governance, Security & Integration- Secure agent tool execution, API calls, and data mutations using least-privilege IAM, VPC Service Controls, Secret Manager, and zero-trust safety guardrails.
- Expose agent capabilities via asynchronous FastAPI / gRPC microservices and register deployed agents into enterprise portals.
- Track rapid advancements in the Google agent ecosystem, including new ADK features, multimodal agents, and reasoning paradigms.
Preferred Knowledge, Skills, and AbilitiesCore Agent & Technical Proficiency (ADK + Python)- Python Mastery: Expert proficiency in modern Python, asynchronous programming (asyncio), typing, structured data modeling, and web framework development (FastAPI).
- Google Agent Development Kit (ADK): In-depth experience with the google-adk Python SDK, including Task APIs, graph workflow runtimes, multi-agent coordination (sequential, parallel, loop, hierarchical), and session/memory layers.
- Google Agent CLI (agents-cli / adk): Hands-on experience driving the Agent Development Lifecycle via CLI (scaffolding, playground interactive testing, evaluation benchmarking, and automated deployment).
- LLM & Tool Ecosystem: Strong understanding of Gemini foundation models, tool definitions (OpenAPI specifications, custom Python functions), and Model Context Protocol (MCP).
GCP AI & Data Stack- Vertex AI Platform: Hands-on experience with Vertex AI Agent Engine, Vertex AI Studio / Model Garden, Vertex AI Vector Search, and Vertex AI Endpoints.
- GCP Infrastructure: Experience hosting and scaling agents on Cloud Run and Google Kubernetes Engine (GKE).
- Data & Storage: Solid SQL and Python data-handling skills with BigQuery, Cloud Firestore, and Cloud Storage.
Software Engineering & Agentic Operations (LLMOps)- Agent Testing & Evaluation: Experience designing structured test datasets, running automated evaluation suites (agents-cli eval run), and implementing prompt and context optimization pipelines.
- DevOps / CI/CD: Proven background with Docker, Artifact Registry, and CI/CD automation (Cloud Build) for agent deployment.
- Observability & Debugging: Practical experience with step-by-step agent execution tracing, state inspection, and telemetry via Cloud Trace and Cloud Logging.
- Security & Guardrails: Experience defending against prompt injection, unauthorized tool execution, and state manipulation in autonomous agent systems.
Collaboration & Prototyping- Ability to translate complex business logic and multi-step workflows into autonomous agent architectures.
- Experience creating internal agent prototypes and test benches using the Agent CLI Playground and interactive Python interfaces.
- Strong communication skills to clearly explain agentic capabilities, trade-offs, and tool-invocation flows to both technical and non-technical stakeholders.
Minimum Education:Bachelor's degree in Computer Science, Data Science, Engineering, or related field is required; Master's is highly preferred.
Minimum Experience:Needs 3-5+ years of dedicated experience designing and shipping ML models to production. Should have led the design of a significant ML-powered feature.
Preferred Qualifications: Pay Transparency: Pay: Plano, TX / Pittsburgh, PA: $122,778 to $165,750/annually. Memphis, TN: $116,639 to $157,463/annually.
Additional Details: For details on our comprehensive benefits, click here. 
