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.
- Ensure AI solutions support auditability, explainability, traceability, and regulatory compliance requirements
- Implement memory management, context engineering, planning, and multi-step reasoning strategies
- Define and track quality metrics such as groundedness, faithfulness, relevance, task completion rate, and user satisfaction
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:Needs 3-5+ years of dedicated experience designing and shipping ML models to production. Should have led the design of a significant ML-powered feature.
This position is eligible for remote work and may be located anywhere within the United States excluding AK, HI and U.S. territories, however if you live within the 50 miles radius of a campus you will be required to work at a FedEx campus location several times per week.
USA: $9,208.38/mo - $20,872.33/mo, CO: $9,208.38/mo - $20,002.65/mo, CA: $9,719.96/mo - $16,575.08/mo, NJ: $9,719.96/mo - $15,551.93/mo, OH & VT: $9,719.96/mo - $16,523.93/mo, MN: $9,719.96/mo - $19,132.97/mo, IL & NV: $9,719.96/mo - $20,002.65/mo, MD, NY & WA: $9,719.96/mo - $20,872.33/mo, MA: $10,231.53/mo - $20,872.33/mo, RI: $11,254.69/mo - $19,132.97/mo, CT: $11,254.69/mo - $20,002.65/mo, DC & HI: $11,766.26/mo - $20,002.65/mo, NYC: $11,766.26/mo - $20,872.33/mo
Preferred Qualifications: Pay Transparency: Pay: Additional Details: Pay Transparency:The compensation listed reflects the pay range or rate of pay reasonably expected for this posted position at the posted location or locations. If this opportunity includes multiple job levels, the pay information represents the ranges for each level in that job family. Actual pay is determined by several job-related factors permitted by law and relevant to the position, including, but not limited to, experience relative to the job, tenure, market level, pay at the location for this job, performance, schedule, and work assignment. In California, the compensation listed reflects the range or rate of pay reasonably expected for this posted position upon hire. In New Jersey, any compensable Security and Walk time will be paid to non-exempt/hourly employees at the state minimum wage.
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