Platform Engineering Advisor

Domicile Information

This is a hybrid position in Plano, TX (first preference), Memphis, TN, or Pittsburgh, PA. Candidates residing within 50 miles of a Fed Ex campus will be required to work on-site at a Fed Ex location several times per week.

As a Platform Engineer you will be responsible for the development, integration and management of technical frameworks deployed within private, public, and/or hybrid cloud platforms ensuring performance and scalability of complex analytical applications. In this role the candidate must be able to identify various patterns for data acquisition, processing, and management with respect to differing data types, volume, velocity, and accessibility requirements supporting BI /AI /ML based applications.

Building upon pattern identifications this person will be responsible for collaborating with engineers and architects to develop analytical frameworks which will be the foundation of Fed Ex's Data and Analytics Platform. The summation of the frameworks will ensure the platform, data, and derived BI /AI /ML applications are scalable, reliable, and performant while balancing security, maintainability, reliability, and operational excellence.

• Write clean, efficient, modular, and well-documented Python code to develop and implement machine learning, deep learning, and generative AI models supporting diverse business use cases.
• Build scalable data engineering, feature transformation, and preprocessing workflows using Big Query
  , Cloud Dataflow (Apache Beam), and Cloud Storage (GCS).
• Continuously optimize model inference latency, throughput, and compute resource utilization on GCP infrastructure (GPUs/TPUs).

• Design, develop, and maintain automated ML pipelines for data extraction, training, hyperparameter tuning, evaluation, and deployment using Vertex AI Pipelines (Kubeflow Pipelines / TFX).
• Package and deploy models to production using Vertex AI Endpoints
  , Cloud Run
  , or Google Kubernetes Engine (GKE) with containerized Python runtimes.
• Implement end-to-end MLOps observability using Vertex AI Model Monitoring
  , Vertex ML Metadata
  , Cloud Logging
  , and Cloud Monitoring to detect data/concept drift, anomalous inputs, and latency regressions.
• Define and own the operational readiness of AI services by implementing Service Level Objectives (SLOs) (e.g., p50/p95/p99 latency, uptime) and automated alerting.

• Partner closely with Data Scientists and Research Engineers to transition experimental Python prototypes and Jupyter notebooks (Vertex AI Workbench) into robust, production-grade microservices.
• Expose AI models via high-performance REST/gRPC APIs using modern Python frameworks (e.g.,
  FastAPI
  ) and integrate them into enterprise applications and data pipelines.
• Ensure transparency and interpretability of model predictions using Vertex Explainable AI (Feature Attributions, Integrated Gradients, SHAP).

• Enforce enterprise security, compliance, and responsible AI governance across GCP workloads using IAM best practices, VPC Service Controls, and Secret Manager.
• Evaluate and prototype emerging GenAI capabilities within the GCP ecosystem-including Gemini models via Vertex AI Model Garden
  , Vertex AI Agent Builder
  , and fine-tuning techniques.

• Advanced Python: Mastery of modern Python (3.10+), object-oriented programming, asynchronous programming (asyncio), API development (FastAPI/Flask), packaging, and testing (pytest).
• Machine Learning & Deep Learning: Deep expertise in ML algorithms and modern deep learning frameworks (
  Py Torch ,
  Tensor Flow
  , JAX
  , or Hugging Face Transformers
  ).
• Generative AI & LLMs: Proven experience building LLM-powered applications, RAG pipelines, and agentic workflows using Vertex AI Studio / Model Garden (Gemini),
  Vertex AI Vector Search
  , and orchestration frameworks like Lang Chain
  , Lang Graph
  , or Llama Index
  .
• Data Manipulation & Querying: High proficiency in SQL
  , Big Query (including Big Query ML), and Python data libraries (
  Pandas
  , Polars
  , Py…