Summary:The AI Engineer is responsible for designing, developing, deploying, and maintaining artificial intelligence and machine learning solutions that support intelligent automation, predictive insight, and advanced analytics across the enterprise. As a hands-on builder, this role applies software engineering principles to write production-quality code, build scalable AI systems, including AI Agents and data pipelines, and integrate AI models into new and existing business applications. The AI Engineer collaborates closely with Data Scientists, Data Engineers, ML Ops Engineers, and Platform teams to bring machine learning models from prototype to production. A critical part of this function is to ensure that AI use cases are transitioned from experimentation into reliable, governed, and business-ready solutions by owning their complete operational readiness. This includes implementing robust observability, defining Service Level Objectives (SLOs), and establishing clear incident response and rollback strategies for all AI services.
Job Responsibilities:Model Development & Implementation
- Write clean, efficient, and well-documented code to develop and implement machine learning and AI models that support various business use cases.
- Implement data engineering and preprocessing workflows required for model inputs.
- Continuously optimize the performance and scalability of AI applications and models.
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/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.
Domicile Information The preferred location for this hybrid position is Memphis, TN. However, we will consider candidates based elsewhere within the United States (excluding Alaska, Hawaii, and U.S. territories). Candidates residing within 50 miles of a FedEx campus will be required to work on-site at a FedEx location several times per week.
Pay Transparency: USA: $8,007.29/mo - $18,149.85/mo, CO: $8,007.29/mo - $17,393.61/mo, CA: $8,452.14/mo - $14,413.11/mo, NJ: $8,452.14/mo - $13,523.42/mo, ME, OH & VT: $8,452.14/mo - $15,124.88/mo, MN: $8,452.14/mo - $16,637.36/mo, IL & NV: $8,452.14/mo - $17,393.61/mo, MD, NY, VA & WA: $8,452.14/mo - $18,149.85/mo, MA: $8,896.99/mo - $18,149.85/mo, RI: $9,786.68/mo - $16,637.36/mo, CT: $9,786.68/mo - $17,393.61/mo, DC & HI: $10,231.53/mo - $17,393.61/mo, NYC: $10,231.53/mo - $18,149.85/mo
Preferred Qualifications: Pay Transparency: This compensation range is provided as a reasonable estimate of the current starting salary range for this role across all potential locations. If this opportunity includes multiple job levels, the range is a reasonable estimate of the current starting salary for the lowest level to the current starting salary of the highest level. Actual starting pay would be determined by experience relative to the job, market level, pay at the location for this job and other job-related factors permitted by law. An employee may be eligible for additional pay, premiums, or bonus potential. The Company offers eligible employees health, vision and dental insurance, retirement, and tuition reimbursement.
Pay: Additional Details: For details on our comprehensive benefits, click here. 
