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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 Overview
The Forward Deployed AI Engineer is a hands-on builder who turns high-value business opportunities into secure, production-ready AI solutions. This role partners directly with business teams, architects, data teams, and platform owners to prototype, build, integrate, test, deploy, and continuously improve AI applications, agents, automations, and retrieval-augmented generation solutions that improve efficiency, decision quality, and measurable business value across The Andersons.
Key Responsibilities
- Build and deploy enterprise AI applications, agents, and automations using Microsoft Copilot Studio, Power Automate, Azure OpenAI, Python, APIs, and modern application frameworks.
- Work with business teams to identify manual workflows, define success measures, and convert opportunities into usable AI-powered solutions.
- Design and implement RAG solutions that connect AI experiences to trusted enterprise systems, documents, data sources, and approved knowledge repositories.
- Integrate solutions with Microsoft Graph, Microsoft 365, SharePoint, Teams, Dataverse, SQL, SAP Datasphere, and other business platforms.
- Develop prompts, connectors, orchestration logic, reusable components, and implementation patterns that accelerate delivery and supportability.
- Validate solution quality through testing, monitoring, AI evaluation, feedback loops, security review, and post-deployment improvement.
- Deploy and operate solutions in Azure using source control, CI/CD, logging, observability, cost awareness, security, and governance practices.
- Partner with architects, data teams, and stakeholders to explain solution performance, adoption needs, and value realization.
What is Expected of You and Others at This Level
- In-depth knowledge and experience
- Uses existing solutions to resolve complex issues
- Works independently; receives minimal guidance
- Acts as a resource for colleagues with less experience
Competencies Expected of You and Others at This Level
- Manages Conflict - Handles conflict situations effectively with a minimum of noise
- Communicates Effectively - Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences
- Decision Quality - Makes good and timely decisions that keep the organization moving forward
- Demonstrates Self-Awareness - Using a combination of feedback and reflection to gain productive insight into personal strengths and weaknesses
- Directs Work - Provides clear direction, delegation, and removes obstacles to get work done
- Resourcefulness - Secures and deploys resources effectively and efficiently
Minimum Qualifications & Skills
- Bachelor’s Degree in Computer Science, Information Systems, Software Engineering, Data Science or related field required
- 5-10 years’ relevant experience required
- 2 years building AI, Copilot, generative AI, automation, or machine learning-enabled solutions for production development or enterprise pilots
- Experience turning business workflows into working applications, agents, integrations, or automations that deliver measurable operational value
- Experience integrating enterprise solutions with business systems, data platforms, APIs, and governed cloud environments
Technical Skills Required
- Python development for AI-enabled applications, services, agents, and automations.
- Microsoft Copilot Studio, Power Automate, Azure OpenAI, and modern AI application development patterns.
- REST APIs, secure authentication, connector design, tool/function calling, and enterprise integration patterns.
- Prompt engineering, orchestration logic, agent workflows, human-in-the-loop processes, and multi-step automation design.
- RAG patterns including ingestion, chunking, embeddings, retrieval, grounding, citations, evaluation, and feedback loops.
- Microsoft Graph, Microsoft 365, SharePoint, Teams, Dataverse, SQL, Azure application development, monitoring, and operational support.
- Data governance, data quality, trusted-source practices, and business value measurement.
Technical Skills Preferred
- C#/.NET, TypeScript, or JavaScript for enterprise application integration.
- Vector databases, semantic search, document intelligence, knowledge management, or enterprise search architecture.
- SAP Datasphere or SAP integration experience.
- Docker, infrastructure automation, advanced monitoring, or observability tools.
- Microsoft Azure, Power Platform, or AI-related certifications.
- Experience delivering AI or automation solutions in regulated, security-conscious, or governance-driven enterprise environments.
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.*
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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