AI/ML Engineer

Bayer•Tulsa, OK

11d•$117,000 - $171,000•Remote

About The Position

As an AI/ML Engineer within Bayer Crop Science’s Product Supply organization, you will design, develop, and deploy AI systems and machine learning models to automate processes and solve business problems. You will provide strategic, analytical, and technical expertise to solve critical business problems based on data, and will help collect, clarify, and translate business requirements into an analytical use cases. You will also be responsible for creating models including data collection and analysis, defining information requirements, maintenance and enhancements, to help drive key business.

Requirements

BA/BS degree in Computer Science + 8 years hands-on experience designing, building, deploying, and maintaining end-to-end or related field OR 15+ years' experience in data engineering or related field
AI/ML systems in production environments.
Solid knowledge of backend system design, APIs, CI/CD pipelines, agent-based workflows, and production support for scalable AI/ML platforms.
Fluency in multiple coding languages including SQL and Python and ML frameworks.
Demonstrated ability to coalesce group thinking into definable projects and holding team members accountable for their responsibilities
History of meeting tight deadlines and providing accurate estimates of time required to complete complex tasks
Experience building and deploying production ML system using ML algorithms, deep learning, and statistical modeling
Strong problem-solving and analytical thinking
Effective communication with both technical and non-technical audiences
Comfort with ambiguity and a bias toward experimentation

Nice To Haves

Fluency in AWS, GCP, Gemini, and Copilot preferred, including containerization technologies and orchestration tools
Advanced Excel skills preferred

Responsibilities

Design, build, and operate end-to-end AI/ML and agent-based systems, from problem definition and model development to production deployment, monitoring, and continuous improvement to solve business problems.
Focus on simulating human learning activities, improving system performance through data analysis, and developing deep learning frameworks and systems.
Collaborate with data engineers to build robust data pipelines and ensure high-quality training data
Design scalable, reliable services on major cloud platforms; strong CI/CD, observability, and operational excellence.
Translate customer requirements to business solutions using data pipelines and statical models.
Build & maintain scalable ML infrastructure, including training pipelines, feature stores, and model serving systems
Contribute to MLOps best practices, including CI/CD for ML, model versioning, and A/B testing frameworks
Create exploratory analysis, model design & training, validation, feature engineering, production handoff to drive business optimization
Responsible for constructing, studying, and training algorithms that learn from complex, high-dimensional data to uncover patterns and develop practical predictive models and applications.
Document architectures, experiments, and results clearly for technical and non-technical stakeholders to support current work and any retraining for the future.
Data, model, and agent pipeline engineering (e.g., workflow orchestration, model lifecycle management, automated retraining/rollouts).
Orchestration and integration across components (agent frameworks, containers, web services/APIs, distributed systems).