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About This Role
JOB INFORMATION:
Job Title: Senior AI Engineer Department(s): 5072 - Information Technology Location(s): Garden City Terminal FLSA Status: Exempt
JOB OVERVIEW:
Under general supervision of the Director of IT – Application Development, the Senior AI Engineer designs, develops, and maintains scalable data pipelines, cloud-based data platforms, and AI/ML solutions that support enterprise analytics and business decision-making. The incumbent collaborates with business and technical stakeholders to define analytical objectives, develop research frameworks, and gather, integrate, and analyze complex structured and unstructured data. Responsibilities include designing, training, validating, deploying, and monitoring artificial intelligence (AI), machine learning (ML), and statistical models; implementing ETL/ELT processes, MLOps, and CI/CD practices; ensuring data quality, security, and performance; and communicating data-driven insights and recommendations through reports and presentations. The Senior AI Engineer also provides technical leadership and guidance to software engineers, analytics professionals, and operations specialists throughout the data engineering and AI solution lifecycle.
Upholds the values of the GPA to promote Safety, Integrity, Respect, Community, Creativity and Opportunity. Promotes a safe work environment by participating in safety initiatives and reporting unsafe conditions and behaviors.
JOB DUTIES:
- Defines optimal AI/ML, predictive, and statistical models and research methodologies to address business problems.
- Performs exploratory data analysis and preprocessing of structured and unstructured data to ensure quality, completeness, and readiness for modeling.
- Designs, develops, and maintains scalable data pipelines, ETL/ELT processes, cloud-based data platforms, and model monitoring procedures to support AI/ML solutions and long-term model performance.
- Determines model features, parameters, and training strategies; trains, validates, evaluates, and optimizes AI/ML and statistical models.
- Deploys, monitors, and optimizes production AI/ML solutions and data pipelines to ensure reliability, scalability, and performance.
- Translates complex analytical findings into clear, actionable insights through written reports, dashboards, and oral presentations to both technical and non-technical audiences.
- Evaluates data engineering, AI/ML, cloud, and analytics technologies, recommending improvements and selecting appropriate tools and platforms.
- Conducts stakeholder interviews and data discovery activities to define analytical objectives and identify relevant data sources.
- Prepares project scoping documents including feasibility assessments, data requirements, and resource estimates for management review.
- Provides technical guidance and mentorship to Analysts, Operations specialists, software engineers, and other technical staff throughout the data and AI solution lifecycle.
- Ensures compliance with data governance, information security, privacy requirements, and responsible AI practices in all data and modeling activities.
- Performs other duties as assigned
- Complies with all policies and standards
JOB REQUIREMENTS:
Knowledge, Skills, & Abilities
- Expertise in Python required, with experience using data science and machine learning libraries such as Pandas, Scikit-learn, TensorFlow, or PyTorch. High
- Advanced knowledge of statistical modeling, predictive analytics, machine learning, and AI techniques, including regression, classification, time series analysis, tree-based methods, and deep learning. High
- Advanced SQL proficiency and experience designing, developing, and maintaining ETL/ELT processes and scalable data pipelines required. High
- Experience building, deploying, monitoring, and maintaining AI/ML models in production environments, including familiarity with MLOps, CI/CD, and model lifecycle management. High
- Experience with cloud-based data platforms and services (AWS, Azure, or Google Cloud Platform), cloud data warehouses such as BigQuery, and data engineering technologies such as Databricks, Apache Spark, or similar platforms preferred. High
- Strong analytical, problem-solving, and data visualization skills, including experience with Tableau, Power BI, or comparable tools. High
- Experience using AI-assisted development and agentic coding tools (e.g., Claude Code, Cursor, GitHub Copilot) to improve software development and data science productivity desired. High
- Excellent written and verbal communication skills with the ability to translate complex technical concepts and analytical findings into actionable recommendations for both technical and non-technical stakeholders. High
Experience
- Minimum of five (5) years of progressively responsible experience designing, developing, and deploying enterprise data engineering and AI/ML solutions, including data pipelines, cloud-based data platforms, predictive modeling, and production AI/ML systems. Required
- Experience working in cloud environments and supporting enterprise analytics and data-driven decision-making Required
Education
- Required Bachelor's Degree Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Systems, or a closely related quantitative field
- Preferred Master's Degree master's degree in Data Science, Computer Science, Artificial Intelligence, Statistics, or a related field
Licenses & Certifications
- TWIC - Transportation Worker Identification Credential Required
- Driver's License - Valid state driver's license Required
Note: This job description is not necessarily an exhaustive list of all duties, responsibilities, job requirements, or working conditions associated with the job. Management reserves the right to revise the job or to require the performance of different or additional tasks. The employee must be able to perform the essential functions of the job, with or without reasonable accommodation, an without direct threat to the health or safety of others.
Revised: 8/26/2026
Mission: To empower entrepreneurs, strengthen industries, sustain communities, and fortify families by relentlessly striving to accelerate global commerce.
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 Georgia Ports Authority, 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. Senior-level AI roles across all categories have a median of $230,000.
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.
Georgia Ports Authority AI Hiring
Georgia Ports Authority has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Savannah, GA, US.
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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