ML Pipelines & Operations (MLOps) - Design, develop, and maintain scalable ML pipelines for model 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 in production for performance, data drift, and reliability, and troubleshoot and resolve any issues that arise. Establish and own the operational readiness of all AI services by defining and implementing Service Level Objectives (SLOs) for key metrics, such as p50/p95 latency and availability, and creating robust monitoring and alerting for model drift, latency, and error rates.
Collaboration & Integration - Work closely with Data Scientists to transition experimental models and research prototypes into robust, production-ready systems. Support the integration of AI capabilities into enterprise workflows, applications, and digital platforms. Contribute to the documentation and explainability of model outputs to ensure clarity for business stakeholders.
Governance & Strategy - Ensure all deployed AI systems comply with enterprise governance, fairness, and security standards. Evaluate emerging AI technologies, such as LLMs and generative AI, to assess their applicability to business problems and drive innovation.
Preferred Skills, Knowledge, and Experience
Core Technical & AI Proficiency - Strong coding skills in Python, Java, or C++, including API development and software design. Deep understanding of core machine learning concepts, including classification, regression, clustering, and deep learning architectures. Hands-on experience with modern deep learning frameworks and algorithms (supervised/unsupervised), such as PyTorch, TensorFlow, or similar for building and training complex neural networks. Skills in working with LLMs, prompt engineering, fine-tuning, and using frameworks like LangChain and LangGraph to build RAG (Retrieval-Augmented Generation) systems. Handling data wrangling, SQL, data warehousing, and ETL pipelines to prepare data for models.
End-to-End ML Model Lifecycle - Proven experience in the end-to-end model lifecycle: developing, training, and deploying machine learning models from prototype to production. Mastery of data preprocessing, feature engineering, and model evaluation techniques to ensure robust and accurate model performance. Demonstrated ability to build and optimize scalable data pipelines for training and evaluating machine learning models. Strong knowledge of both SQL and NoSQL databases for querying and managing data for AI applications.
Software & MLOps Engineering - Solid foundation in software engineering best practices, including version control (Git), automated testing, and CI/CD pipelines. Hands-on experience with containerization using Docker and container orchestration with Kubernetes for scalable deployment. Expertise in MLOps observability, including model monitoring to track performance and drift, and establishing model/version lineage, telemetry, and traceability. Experience implementing advanced testing and deployment strategies, including canary/shadow deployments and comprehensive test suites (unit, integration, adversarial, regression). Demonstrated ability to integrate AI models and services into enterprise applications by building and consuming RESTful APIs.
Cloud & Infrastructure - Proficiency with at least one major cloud platform (GCP, AWS, Azure) and its associated AI/ML services (e.g., Vertex AI, SageMaker, Azure ML). Experience with big data technologies, such as Apache Spark or similar, for processing large-scale datasets in a cloud environment.
Collaboration & Frontend Development - Strong problem-solving and analytical skills, with the ability to collaborate effectively in an Agile development environment. Excellent communication skills to articulate complex technical concepts to both technical and non-technical stakeholders. Experience with modern frontend JavaScript frameworks such as React, Vue.js, Angular or similar for building user-facing applications that consume AI models.
Minimum Education
Bachelor’s degree in Computer Science, Data Science, Engineering, or related field is required; Master’s is highly preferred.
Minimum Experience
Must have independently built, trained, and iterated on multiple ML models. This includes 2+ years of hands-on experience with a deep learning framework.
Pay Range
Plano, TX: $8,896.99 / mo - $12,010.93 / mo
Pittsburgh, PA: $8,896.99 / mo - $12,010.93 / mo
Memphis, TN: $8,452.14 / mo - $11,410.38 / mo
Preferred Qualifications
Pay Transparency
Pay: Plano, TX: $8,896.99 – $12,010.93; Pittsburgh, PA: $8,896.99 – $12,010.93;Memphis, TN: $8,452.14 – $11,410.38
Additional Details
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.
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