AI Engineer, Industrial

IFF•Wilmington, DE

4d•Remote

About The Position

Are you passionate about innovation that transforms everyday products into extraordinary experiences? IFF is a global leader in flavors, fragrances, food ingredients and health & biosciences, delivering sustainable innovations that elevate everyday products. Health & Biosciences: Channeling our passion for nature and bioscience into sustainable, life-enhancing technologies that power innovative solutions across healthcare, food, consumer and industrial markets. The role is remote-based with regular travel ; travel may reach up to ~35% annually depending on project scope and deployment phases, with occasional intensive periods (e.g., 2–3 consecutive weeks on-site) . Multiple locations may be considered subject to business needs, local employment requirements, and candidate profile . Be part of a creative, solution-oriented, and collaborative team where together we can achieve greatness and make a real impact. Your potential is our inspiration.

Requirements

Master’s, or PhD degree in Chemical Engineering, Biochemical Engineering, Bioinformatics, Process Control, Computer Science, Data Science or Applied Mathematics.
Demonstrated industrial experience with a proven track record of delivering measurable business impact and driving outcomes in production or operational environments
Proven, hands-on experience with Python and modern machine learning workflows, including data preparation, modeling, validation, deployment, and monitoring.
Experience developing end-to-end AI or machine learning applications, not only exploratory notebooks, with an ability to move from prototype to reliable solution including demonstrated experience deploying solutions in real industrial or production environments .
Practical understanding of frontier model capabilities and limitations, including prompt design, agentic AI, retrieval-augmented generation, evaluation, hallucination reduction, and human-in-the-loop workflows.
Experience working with structured and unstructured data, such as time-series data, scientific data, engineering data, documents, knowledge bases, or complex operational datasets.
Knowledge of methods such as mathematical optimization, process control, forecasting, anomaly detection, recommendation systems, natural language interfaces, classification, deep learning, or predictive analytics.
Familiarity with software engineering practices such as version control, testing, application programming interfaces, containers, continuous integration and continuous delivery, and maintainable code design.
Ability to communicate clearly with both technical and non-technical audiences, including explaining model outputs, uncertainty, assumptions, control logic, optimization trade-offs, and practical implications.
A practical, delivery-oriented mindset with curiosity for industrial, scientific, optimization, and process control problems and a strong focus on usefulness, trust, and adoption.

Nice To Haves

Experience with knowledge graphs, ontologies, semantic modeling, graph databases, or retrieval-augmented generation for scientific, industrial, or operational use cases.
Experience in manufacturing, process development, industrial operations, supply chain, biomanufacturing , bioinformatics, chemical processes, advanced process control, or mathematical optimization in technical environments.
Experience with model registries, experiment tracking, observability, prompt and version management, evaluation frameworks, cloud platforms, or production machine learning systems.

Responsibilities

Develop practical AI, machine learning and optimization solutions that help technical and operational teams make complex industrial data easier to understand, explore, and act on.
Apply frontier AI models, including large language models, foundation models, and multimodal approaches, to create reliable tools for search, analysis, knowledge discovery, and decision support.
Build AI-enabled applications that combine models, data pipelines, prompts, agents, evaluations, optimization logic, and user feedback into solutions that are useful, explainable, and maintainable.
Design and use knowledge graphs, ontologies, and structured domain knowledge to connect process data, experimental data, documents, terminology, assets, materials, and business context.
Work with domain experts to translate scientific, engineering, process control, and operational questions into data products, model workflows, optimization approaches, and user-friendly analytics experiences.
Develop robust pipelines for structured and unstructured data, including time-series data, laboratory data, manufacturing data, documents, and scientific or technical knowledge sources.
Contribute to model validation, monitoring, documentation, governance, and continuous improvement so that deployed solutions can be trusted in real industrial environments.
Collaborate across data science, engineering, digital technology, operations, and business teams to deliver solutions that create measurable value and are designed for adoption.