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About This Role
As a North American leader in agribusiness and renewable fuels, The Andersons operates in a unique space, bringing together agriculture, energy, and global markets. From the farm to customers around the world, we help move food, feed, and fuel where it’s needed most.
Our business spans the full value chain, which means the work we do directly supports farmers, customers, and communities every day. Whether it’s helping producers succeed, delivering essential products, or advancing more sustainable energy solutions, our impact is both local and global.
What truly sets us apart is how we work together. We are a performance-driven, people-powered organization where employees are owners, sharing in our success and helping shape our future. We invest in our team through development opportunities, meaningful career paths, and a culture that encourages growth, collaboration, and continuous improvement.
At the core of everything we do is our Statement of Principles. We are committed to providing extraordinary service to our customers, helping our employees improve, supporting our communities, and increasing the value of the company for our shareholders. Position Summary
This position is responsible for defining and maintaining the organization’s overall architecture framework, ensuring that technology strategy aligns with business objectives. This role provides strategic guidance for technology investments, promotes architectural coherence across systems and processes, and drives innovation through enterprise-level design principles. The Enterprise Architect must have a holistic view of the organization’s technology landscape and the ability to influence stakeholders at all levels.
Key Responsibilities
- Develop and maintain the enterprise architecture strategy, standards, and roadmap for digital platforms, custom applications, portals, integration touchpoints, automation capabilities, AI-enabled business solutions, and the internal AI development capabilities needed to support them.
- Provide architecture leadership across the full solution lifecycle, ensuring digital and AI solutions are scalable, secure, resilient, maintainable, cost-effective, and aligned with business priorities, enterprise technology direction, and a supported path from experimentation to production.
- Translate business needs into practical architecture direction by defining target-state designs, transition architectures, platform recommendations, implementation guidance, and developer enablement approaches for product, engineering, and delivery teams.
- Establish and promote reusable architecture patterns, reference designs, shared repositories, templates, starter kits, decision frameworks, and design review practices that improve consistency, quality, governance, and speed of delivery across digital and AI initiatives.
- Guide AI platform and application architecture, including retrieval-augmented generation, embeddings and vector search, agent and orchestration patterns, API-based model integration, hosted and locally run model options, observability, security, privacy, compliance, prompt and context handling, and human-review guardrails.
- Assess current digital and AI capabilities, emerging technologies, models, vendor solutions, platform fit, technical debt, modernization opportunities, and trade-offs across capability, cost, latency, data sensitivity, hosting constraints, and operational support to inform roadmaps, investment decisions, standards, and lifecycle management.
- Build prototypes, reference implementations, and proof-of-concepts to validate architectural choices, reduce delivery risk, demonstrate value, and accelerate adoption of effective digital and AI patterns by application teams.
- Partner closely with business stakeholders, enterprise architecture, engineering, security, data, infrastructure, and leadership teams to align solution direction, resolve architecture tradeoffs, mentor developers, and support enterprise adoption.
What is Expected of You and Others at This Level
- Demonstrates expertise in own area within the organization
- Required specialized depth and/or breadth of experience
- Interprets internal or external business issues and recommends solutions/best practices
- Solves complex problems; takes a broad perspective to identify solutions
Competencies Expected of You and Others at This Level
- Balances Stakeholders - Anticipating and balancing the needs of multiple stakeholders
- Business Insight - Applies knowledge of the business and the marketplace to advance the organization’s goals
- Drives Results - Consistently achieves results, even under challenging circumstances
- Manages Ambiguity - Operates effectively in uncertain or unclear situations
- Organizational Savvy - Maneuvers comfortably through complex policy, process, and people related organizational dynamics
Education and Experience
- Bachelor’s Degree required in Computer Science, Information Technology, or a related field. Master’s Degree preferred
- 7-10 years’ relevant experience as an Enterprise or Domain Architect required
- Experience defining architecture direction for digital platforms, customer-facing applications, portals, or internal digital products.
- Hands-on experience designing, implementing, or governing AI or machine learning enabled systems in a complex enterprise environment.
- Practical experience working with a range of AI models across hosted and locally run environments, with a working understanding of the capability, cost, performance, security, and data-handling trade-offs between them.
- Working knowledge of model lifecycle management, model evaluation and benchmarking, and monitoring of AI workloads in production.
- Experience establishing standards, patterns, or guardrails for AI usage in application development.
- Excellent organizational skills and attention to detail
- Excellent written and verbal communication skills
- Proficient in Microsoft Office Suite or similar software
Physical Requirements and Working Environment
- Prolonged periods of sitting at a desk and working at a computer
This job description is intended to give you a general understanding of the role and the type of work involved, but it may not include every responsibility or requirement. Employment with The Andersons, Inc. is at will*, which means both you and the Company can end the employment relationship at any time, with or without cause or notice, consistent with applicable law, nothing in this description should be viewed as a contract.*
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Disclaimer:
The Andersons, Inc. is proud to be an equal opportunity employer and welcomes all qualified applicants. We are committed to creating an inclusive environment and consider candidates without regard to any status protected by law, including race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability.
We also prioritize a safe and healthy workplace. The Company maintains a drug-free work environment and may require drug, alcohol, and/or medical testing depending on the role.
To ensure a consistent hiring process, The Andersons, Inc. does not accept unsolicited resumes from agencies that do not have a formal agreement in place and is not responsible for any related fees.
Finally, we want to help you stay informed and safe during your job search. Please note that The Andersons only uses company email addresses, which contain “@andersonsinc.com” or"@theandersons.com", to communicate with candidates via email. If you are contacted by someone about an open job at The Andersons, please verify the domain of the sender’s email address. If something seems unusual, we encourage you to verify communications and report any concerns through [email protected]
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 The Andersons, 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.
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.
The Andersons AI Hiring
The Andersons has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Maumee, OH, 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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