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About This Role
Domicile Information
This is a hybrid position in Plano, TX (first preference), Memphis, TN, or Pittsburgh, PA. Candidates residing within 50 miles of a FedEx campus will be required to work on-site at a FedEx location several times per week.
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.
Essential Functions
Agent Development & Orchestration (ADK & Python)
- Architect, develop, and maintain multi-agent systems, deterministic workflow agents, and autonomous tool-calling pipelines using the Google Agent Development Kit (ADK) in Python.
- Use the Google Agent CLI (agents-cli / adk) to scaffold, run, test, evaluate, and package agent projects across development, staging, and production environments.
- Implement complex agentic patterns including graph-based workflow runtimes, hierarchical multi-agent delegation, human-in-the-loop (HITL) tool confirmations, and state/session memory management.
- Build custom agent tools, Model Context Protocol (MCP) tool integrations, and API connectors powered by foundation models on Vertex AI.
Retrieval & Semantic Data Pipelines
- Design and implement high-performance RAG (Retrieval-Augmented Generation) architectures integrated with ADK using Vertex AI Vector Search, BigQuery Vector Search, and Cloud Storage (GCS).
- Build scalable ingestion, chunking, and embedding pipelines using Python, Cloud Dataflow (Apache Beam), and Cloud Run Jobs.
- Integrate structured enterprise databases (BigQuery, Cloud SQL / AlloyDB pgvector, Firestore) as tool-accessible datastores for autonomous agents.
Agent Deployment & LLMOps
- Deploy and operationalize ADK agents seamlessly to Vertex AI Agent Engine (Reasoning Engines), Cloud Run, and Google Kubernetes Engine (GKE) using the Agent CLI and containerized Python runtimes.
- Establish automated evaluation suites using agents-cli eval, Vertex AI GenAI Evaluation Service, and continuous regression testing for reasoning, tool fidelity, and context relevance.
- Implement production-grade agent observability, session telemetry, step-by-step tracing, and latency monitoring using Cloud Trace, Cloud Logging, and Vertex AI Model Monitoring.
- Define and own Service Level Objectives (SLOs) (e.g., TTFT, p50/p95 execution latency, tool invocation error rates, agent task completion rates).
Governance, Security & Integration
- Secure agent tool execution, API calls, and data mutations using least-privilege IAM, VPC Service Controls, Secret Manager, and zero-trust safety guardrails.
- Expose agent capabilities via asynchronous FastAPI / gRPC microservices and register deployed agents into enterprise portals.
- Track rapid advancements in the Google agent ecosystem, including new ADK features, multimodal agents, and reasoning paradigms.
Preferred Knowledge, Skills, and Abilities
Core Agent & Technical Proficiency (ADK + Python)
- Python Mastery: Expert proficiency in modern Python, asynchronous programming (asyncio), typing, structured data modeling, and web framework development (FastAPI).
- Google Agent Development Kit (ADK): In-depth experience with the google-adk Python SDK, including Task APIs, graph workflow runtimes, multi-agent coordination (sequential, parallel, loop, hierarchical), and session/memory layers.
- Google Agent CLI (agents-cli / adk): Hands-on experience driving the Agent Development Lifecycle via CLI (scaffolding, playground interactive testing, evaluation benchmarking, and automated deployment).
- LLM & Tool Ecosystem: Strong understanding of Gemini foundation models, tool definitions (OpenAPI specifications, custom Python functions), and Model Context Protocol (MCP).
GCP AI & Data Stack
- Vertex AI Platform: Hands-on experience with Vertex AI Agent Engine, Vertex AI Studio / Model Garden, Vertex AI Vector Search, and Vertex AI Endpoints.
- GCP Infrastructure: Experience hosting and scaling agents on Cloud Run and Google Kubernetes Engine (GKE).
- Data & Storage: Solid SQL and Python data-handling skills with BigQuery, Cloud Firestore, and Cloud Storage.
Software Engineering & Agentic Operations (LLMOps)
- Agent Testing & Evaluation: Experience designing structured test datasets, running automated evaluation suites (agents-cli eval run), and implementing prompt and context optimization pipelines.
- DevOps / CI/CD: Proven background with Docker, Artifact Registry, and CI/CD automation (Cloud Build) for agent deployment.
- Observability & Debugging: Practical experience with step-by-step agent execution tracing, state inspection, and telemetry via Cloud Trace and Cloud Logging.
- Security & Guardrails: Experience defending against prompt injection, unauthorized tool execution, and state manipulation in autonomous agent systems.
Collaboration & Prototyping
- Ability to translate complex business logic and multi-step workflows into autonomous agent architectures.
- Experience creating internal agent prototypes and test benches using the Agent CLI Playground and interactive Python interfaces.
- Strong communication skills to clearly explain agentic capabilities, trade-offs, and tool-invocation flows to both technical and non-technical stakeholders.
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.
Preferred Qualifications:
Pay Transparency:
Pay: Plano, TX / Pittsburgh, PA: $122,778 to $165,750/annually. Memphis, TN: $116,639 to $157,463/annually.
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 [email protected].
Applicants have rights under Federal Employment Laws:
- Know Your Rights
- Pay Transparency
- Family and Medical Leave Act (FMLA)
- Employee Polygraph Protection Act
E-Verify Program Participant: Federal Express Corporation participates in the Department of Homeland Security U.S. Citizenship and Immigration Services’ E-Verify program (For U.S. applicants and employees only). Please click below to learn more about the E-Verify program:
- E-Verify Notice (bilingual)
- Right to Work Notice (English) / (Spanish)
Salary Context
This $116K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At FedEx, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($141K) sits 33% below the category median. Disclosed range: $116K to $165K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
FedEx AI Hiring
FedEx has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Plano, TX, US, Memphis, TN, US. Compensation range: $165K - $165K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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