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About This Role
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.
Additional Job Description
Minimum Education: Bachelor's degree/Equivalent in computer science, information systems and/or equivalent formal training.
Minimum Experience: Five (5+) years of experience in IT information security.
Knowledge, Skills, and Abilities: Skills include IT security and infrastructure. Strong technical and consulting skills, project management capability. Experience with security and risk frameworks, standards and best practices. Strong communication skill.
Domicile
This is a hybrid position located in Plano, TX or Pittsburgh, PA or Memphis, TN. Candidates must live within 50 miles of the campus location. Employees will be required to work at the FedEx campus location several times per week.
Pay Range
Plano, TX: $7,882.48 / mo - $10,444.28 / mo
Pittsburgh, PA: $7,882.48 / mo - $10,444.28 / mo
Memphis, TN: $7,488.36 / mo - $9,922.07 / mo
For details on our comprehensive benefits, click here.
FedEx Dataworks is an Equal Opportunity Employer including, Vets/Disability.
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- Pay Transparency
Dataworks does not discriminate against qualified individuals with disabilities in regard to job application procedures, hiring, and other terms and conditions of employment. Further, Dataworks is prepared to make reasonable accommodations for the known physical or mental limitations of an otherwise qualified applicant or employee to enable the applicant or employee to be considered for the desired position, to perform the essential functions of the position in question, or to enjoy equal benefits and privileges of employment as are enjoyed by other similarly situated employees without disabilities, unless the accommodation will impose an undue hardship. If a reasonable accommodation is needed, please contact [email protected].
Salary Context
This $89K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At FedEx Dataworks, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($107K) sits 49% below the category median. Disclosed range: $89K to $125K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
FedEx Dataworks AI Hiring
FedEx Dataworks has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Memphis, TN, US. Compensation range: $125K - $125K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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