Platform Engineering Advisor

We need to output HTML fragment with sections for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections with real content. We must not repeat job title. Must not include Job ID etc. Remove boilerplate. We need to parse the posting. Key sections: - Domicile Information: location hybrid positions. Could be part of About the role? Might be "Location" but not a required heading. Since we have no heading for location, we could put in a

under About the role maybe. - Summary: description of role. That's About the role. - Essential Functions: list of duties. That's Responsibilities. - Preferred Knowledge, Skills, And Abilities: list of skills. That's Skills. - Minimum Education: Qualifications? Could be Requirements? Actually Minimum Education and Minimum Experience are qualifications. Could be under Qualifications. - Preferred Qualifications: maybe also Qualifications. - Pay Transparency: includes Pay details. So we need a Pay section with the salary ranges. - Benefits: mention "For details on our comprehensive benefits, click here." That's vague; no specific benefits listed. According to rule: do NOT create Benefits section unless posting actually states them. It only says "For details on our comprehensive benefits, click here." That's not specific. So we should not include Benefits section. - Schedule: not mentioned. No schedule details. So omit. - About the role: we have Summary and Domicile Information. Could combine. - Responsibilities: Essential Functions bullet list. - Requirements: maybe Minimum Education and Minimum Experience? Could be Requirements. - Qualifications: maybe Preferred Qualifications? But we already have Requirements. Could combine. Better to follow typical sections: About the role (summary + location), Responsibilities (Essential Functions), Requirements (Minimum Education + Minimum Experience), Skills (Preferred Knowledge, Skills, And Abilities), Pay (salary ranges). Possibly also Qualifications (Preferred Qualifications) but could be merged into Skills or Requirements. However we have both Minimum and Preferred. Could put Minimum under Requirements, Preferred under Qualifications. Let's extract details. About the role: - 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. - Summary: 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 FedEx'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. We can combine into a few paragraphs. Responsibilities (Essential Functions) list: - Model Development & Implementation - 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 BigQuery, Cloud Dataflow (Apache Beam), and Cloud Storage (GCS). - Continuously optimize model inference latency, throughput, and compute resource utilization on GCP infrastructure (GPUs/TPUs). - ML Pipelines & Operations (MLOps) - 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. - Collaboration & Integration - 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). - Governance & Strategy - 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. We need to output as

• for any list of duties/requirements/benefits. So each bullet can be a
• . For sub-bullets? The spec says use
  • for any list of duties/requirements/benefits. It doesn't forbid nested lists? Probably we can keep flat list items, each line as
  • . Could also nest but safer to keep flat. Thus we will create a
    with each essential function as a
    • . Might be long but okay. Skills (Preferred Knowledge, Skills, And Abilities) list: - Core Technical & AI Proficiency (Python-First) - 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 (PyTorch, TensorFlow, 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 LangChain, LangGraph, or LlamaIndex. - Data Manipulation & Querying: High proficiency in SQL, BigQuery (including BigQuery ML), and Python data libraries (Pandas, Polars, PyArrow). - End-to-End ML Lifecycle on GCP - Extensive hands-on experience across the Vertex AI suite: - Model Training (Custom training jobs, distributed GPU training) - Model Registry & Endpoint Hosting (Online, Batch, and Serverless prediction) - Feature Store & Dataset Management - Vertex AI Pipelines & Experiments tracking - Experience building ETL/ELT pipelines using Cloud Dataflow (Apache Beam in Python), Cloud Dataproc (PySpark), or Cloud Composer (Apache Airflow). - Software & MLOps Engineering - CI/CD & Automation: Experience building automated CI/CD workflows for ML using Cloud Build, GitHub Actions, or GitLab CI integrated with Artifact Registry. - Containerization & Orchestration: Strong knowledge of Docker and deployment patterns on Cloud Run or Google Kubernetes Engine (GKE). - Infrastructure as Code (IaC): Working knowledge of provisioning GCP AI/ML infrastructure using Terraform. - Testing & Quality Assurance: Experience implementing comprehensive test suites (unit, integration, load testing with Locust, and LLM evaluation benchmarks). - Cloud & Platform Security - Deep understanding of GCP cloud architecture, including networking (VPCs, private endpoints), service accounts, IAM roles, and data residency/security guardrails. - Collaboration & Application Delivery - Strong problem-solving skills with experience working in Agile/Scrum methodologies. - Strong communication skills to articulate ML architectures and trade-offs to engineering teams and business stakeholders. - (Bonus) Familiarity with rapid UI prototyping tools in Python (Streamlit, Gradio) or frontend frameworks (React, Next.js) to demo and test AI solutions. We'll flatten each bullet as
    • . Requirements (Minimum Education and Minimum Experience): - Minimum Education: Bachelor's Degree/equivalent in computer science, engineering, or information systems and/or equivalent formal training or work experience. - Minimum Experience: Five to seven (5 -7) years’ work experience in Platform Engineering or related field. - Familiarity with conducting end-to end analyses, including data gathering and requirements specification, processing, analysis, and presentations. - Experience providing leadership in a general planning or consulting setting. - Experience as a leader or a senior member of multi-function project teams. - Strong oral and written communication skills. - A related advanced degree may offset the related experience requirements. We'll flatten. Pay: we have Pay: Plano, TX and Pittsburgh, PA: $106,763 to 144,131.16/annually. Memphis, TN: $101,425 to 136,924.56/annually. We'll put in

      . We should not include Benefits or Schedule. Now produce HTML fragment: start with maybe for each heading? The instruction: use

      for section headings. So each section:

      About the role

      then content (

      or

      ). No extra wrappers needed. We must not output markdown code fences. Just raw HTML. Let's craft. We'll need to escape any HTML special characters? Not needed. Let's produce:

      About the role

      This is a hybrid position located 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.

      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 FedEx'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.

      Responsibilities

      • 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 BigQuery, 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