AI Engineer III

DataJobs

Memphis (TN)

Hybrid

USD 114,000 - 146,000

Full time

14 days+

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Job summary

FedEx is seeking an AI Engineer III to design, develop, deploy, and operate AI/ML solutions for intelligent automation and predictive analytics in a hybrid environment.

You will build scalable ML pipelines, collaborate with Data Scientists, and implement MLOps practices to ensure governance, security, and explainability across enterprise systems.

Qualifications

3-5+ years of dedicated experience designing and shipping ML models to production.
Proficiency in Python, Java, or C++ for API development and software design.
Strong understanding of ML concepts including classification, regression, and deep learning architectures.
Hands-on experience with PyTorch and TensorFlow in production environments.
LLM experience including prompt engineering, fine-tuning, and RAG systems.

Responsibilities

Design, develop, deploy, and maintain AI/ML solutions for intelligent automation and analytics.
Write clean, well-documented code to implement AI/ML models.
Create data engineering pipelines for model inputs and preprocessing.
Build scalable ML pipelines for training, validation, and inference.
Collaborate with ML Ops to package and deploy models in enterprise systems.

Skills

Python

Java

C++

PyTorch

TensorFlow

RESTful APIs

Git

Docker

Kubernetes

SQL

Education

Bachelor's degree in Computer Science or related field

Master's degree preferred

Tools

LangChain

LangGraph

SageMaker

Azure ML

Job description

FedEx is hiring an AI Engineer III to design, develop, deploy, and operate AI/ML solutions for intelligent automation and predictive analytics in a hybrid environment.

Responsibilities

Design, develop, deploy, and maintain artificial intelligence and machine learning solutions for intelligent automation, predictive insight, and advanced analytics across the enterprise

Write clean, efficient, and well-documented code to build and implement AI/ML models for business use cases

Create data engineering and preprocessing workflows required for model inputs

Continuously optimize AI application and model performance and scalability

Build and maintain scalable ML pipelines for training, validation, inference, and deployment

Collaborate with ML Ops Engineers to package and deploy models into enterprise systems using established MLOps practices

Monitor deployed models for performance, data drift, and reliability; troubleshoot and resolve production issues

Own operational readiness for AI services by defining and implementing Service Level Objectives (SLOs) for key metrics (including p50/p95 latency and availability)

Implement monitoring and alerting for model drift, latency, and error rates

Partner with Data Scientists to move experimental models and research prototypes into production-ready systems

Support integration of AI capabilities into enterprise workflows, applications, and digital platforms

Contribute to documentation and explainability of model outputs for business stakeholders

Ensure deployed AI systems comply with enterprise governance, fairness, and security standards

Evaluate emerging AI technologies, including LLMs and generative AI, for applicability to business problems and innovation

Support auditability, explainability, traceability, and regulatory compliance requirements

Implement memory management, context engineering, planning, and multi-step reasoning strategies

Define and track quality metrics including groundedness, faithfulness, relevance, task completion rate, and user satisfaction

Requirements

3-5+ years of dedicated experience designing and shipping ML models to production

At least 3 years of experience (minimum)

Coding proficiency in Python, Java, or C++, including API development and software design

Deep understanding of core machine learning concepts: classification, regression, clustering, and deep learning architectures

Hands-on experience with modern deep learning frameworks and algorithms, including supervised/unsupervised methods (e.g., PyTorch, TensorFlow)

LLM experience including prompt engineering, fine-tuning, and building RAG systems with frameworks such as LangChain and LangGraph

Experience with data wrangling, SQL, data warehousing, and ETL pipelines for model-ready datasets

Proven end-to-end model lifecycle experience from prototype to production deployment

Strong skills in data preprocessing, feature engineering, and model evaluation

Proven ability to build and optimize scalable data pipelines for training and evaluation

Strong knowledge of both SQL and NoSQL databases for querying and managing data for AI applications

Software engineering best practices including Git, automated testing, and CI/CD

Containerization experience with Docker and orchestration with Kubernetes

MLOps observability experience: monitoring for performance and drift, plus model/version lineage, telemetry, and traceability

Experience with advanced testing and deployment strategies, including canary/shadow deployments and unit/integration/adversarial/regression test suites

Ability to integrate AI models and services into enterprise applications via RESTful APIs

Proficiency with at least one major cloud platform (GCP, AWS, or Azure) and associated AI/ML services (e.g., Vertex AI, SageMaker, Azure ML)

Experience with big data technologies such as Apache Spark for large-scale dataset processing in a cloud environment

Problem-solving and analytical skills with effective collaboration in an Agile environment

Communication skills to explain complex technical concepts to technical and non-technical stakeholders

Experience using modern frontend JavaScript frameworks (at least one): React, Vue.js, or Angular for user-facing applications that consume AI models