Job description

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. 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. Essential Functions 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. Preferred Knowledge, Skills, and Abilities 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. 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. Preferred Qualifications: Pay Transparency: Pay: Plano, TX and Pittsburgh, PA: $106,763 to 144,131.16/annually. Memphis, TN: $101,425 to 136,924.56/annually Additional Details: Application Criteria: Upload current copy of Resume (Microsoft Word or PDF format only) and answer job screening questionnaire For details on our comprehensive benefits, click here . Federal Express Corporation is an Equal Opportunity Employer including, Vets/Disability. Reasonable accommodations are available for qualified individuals with disabilities throughout the application process. Applicants who require reasonable accommodations in the application or hiring process should contact recruitmentsupport@fedex.com . Applicants have rights under Federal Employment Laws: • Know Your Rights • Pay Transparency • Family and Medical Leave Act (FMLA) • Employee Polygraph Protection Act E-Verify Program Participant: Federal Express Corporation participates in the Department of Homeland Security U.S. Citizenship and Immigration Services’ E-Verify program (For U.S. applicants and employees only). Please click below to learn more about the E-Verify program: • E-Verify Notice ( bilingual ) • Right to Work Notice ( English ) / ( Spanish )

Requirements

Must-haves

Domicile Information
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.
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 ).
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

Nice-to-haves

Preferred Knowledge, Skills, and Abilities
(Bonus) Familiarity with rapid UI prototyping tools in Python ( Streamlit , Gradio ) or frontend frameworks ( React , Next.js ) to demo and test AI solutions.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

accessibilityaimlpythonmachine learninggcpkubernetesrestapillmsqlci/cddockeragilescrumreactnext.js

Platform: paradox
Effort: medium

Benefits

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Vision