Senior AI Engineer

At Bayer were visionaries driven to solve the worlds toughest challenges and striving for a world where Health for all Hunger for none is no longer a dream but a real possibility. Were doing it with energy curiosity and sheer dedication always learning from unique perspectives of those around us expanding our thinking growing our capabilities and redefining impossible. There are so many reasons to join us. If youre hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference theres only one choice.

Senior AI Engineer

POSITION PURPOSE:

We are looking for a highly skilled Azure Local Agentic AI Engineer to join our this role you will be at the forefront of our AI transformation designing and deploying sophisticated agentic AI workflows that operate within our hybrid and edge environments. You will leverage Azure and Azure Local to bring the power of LLMs and autonomous agents closer to our data ensuring optimal performance data privacy and regulatory compliance. You will bridge the gap between cloud-native AI development and on-premises infrastructure building resilient scalable and intelligent systems.

YOUR TASKS AND RESPONSIBILITIES:

1. AI Solution Development

• Lead the design and development of innovative machine learning models and algorithms in cross-functional teams to effectively address specific business challenges.
• Lead the implementation of scalable AI products ensuring alignment with user requirements and strategic business objectives.
• Leverage state-of-the-art cloud technologies (e.g. AWS Azure Google Cloud) to optimize the industrialization of machine learning models and AI solutions for production deployment.
• Provide expert operational support and guidance for machine learning models and algorithms ensuring their reliability and performance in production environments.

1. Data Engineering

• Lead the architecture and design of robust scalable data pipelines in collaboration with data engineers cloud engineers and data scientists enhancing data access and processing capabilities.
• Lead data preprocessing ingestion and transformation activities across hybrid environments (both on-premises and cloud) ensuring adherence to best practices and organizational standards.
• Establish and promote advanced data quality assurance processes including validation checks and proactive data drift detection mechanisms to ensure data consistency and integrity.

1. Data Science

• Mentor and guide team members in the model training process focusing on industrialization through cloud technologies and achieving accuracy reliability through experimentation and iterative improvements.
• Lead the evaluation and tuning of models utilizing metrics and performance benchmarks to refine model parameters and enhance predictive capabilities.

1. Integration and Deployment

• Lead the integration of AI models into existing software systems and workflows ensuring seamless functionality and optimal user experience.
• Direct the deployment of AI solutions into production environments implementing best practices for version control architecture monitoring and ongoing maintenance.

1. Collaboration and Communication

• Maintain comprehensive documentation of AI models algorithms solution architecture and processes to ensure transparency and facilitate knowledge sharing within the team.
• Engage proactively with team members and stakeholders to gather requirements provide insights and deliver effective AI solutions that align with project goals.
• Communicate complex AI concepts and project results effectively to both technical and non-technical stakeholders fostering understanding collaboration and informed decision-making.

1. Research and Innovation

• Stay abreast of emerging AI trends technologies and methodologies actively contributing to the enhancement of team capabilities and innovation.
• Conduct research and experimentation to explore new AI techniques and approaches providing strategic insights that can inform future projects and initiatives.

WHO YOU ARE:

• Masters or Ph.D. degree in Computer Science Information Technology Data Science Artificial Intelligence or a related field or Bachelors with minimum 8 years experience.
• Proven experience as an AI Engineer Machine Learning Engineer or similar role.
• Experience in developing and deploying machine learning models and AI systems.
• Proficiency in programming languages such as Python or R
• Strong knowledge of machine learning frameworks (TensorFlow PyTorch Scikit-learn).
• Experience with natural language processing (NLP) computer vision or other AI techniques
• Familiarity with big data technologies (Hadoop Spark) and cloud platforms (AWS Azure Google Cloud)
• Strong analytical skills and the ability to work with complex datasets.
• Excellent problem-solving skills with a focus on developing innovative AI solutions.
• Excellent verbal and written communication skills.
• Ability to work collaboratively in a cross-functional team environment.
• Strong organizational skills and the ability to manage multiple AI projects simultaneously.
• Experience with Agile methodologies and tools (Azure DevOps Scrum).
• Azure Expertise:Deep understanding of the Azure ecosystem specifically Azure AI Azure Machine Learning Compute Storage Networks and Azure Arc Containers (registry runtimes) Events Functions Azure Identity tooling
• Edge/Hybrid Cloud:Practical experience with hybrid cloud or edge computing infrastructure (e.g. Azure Stack HCI Kubernetes on-premises).
• Programming:Strong proficiency in Python 3 and familiarity with modern AI and automation development stacks and Powershell
• System Architecture:Solid understanding of distributed systems containerization (Docker/Kubernetes) and API design.
• Teams Bot design and implementation: Microsoft Teams Bot integrations; understanding of maintaining and publishing bots React
• DevOps tooling: Good understanding of Bayer DevOps (go/cloud) tools like Git GitHub Terraform Docker Containers Artifactory ADO Container registry Security tools Code Quality tools Makefile
• Databases: Azure Databases (CosmosDB MongoDB SQL Database PostgreSQL)
• Data Engineering: Databricks (Spark/OSP) Log Analytics Kafka

Preferred

• Relevant certifications in AI machine learning or data science (e.g. AWS Certified Cloud / AI practitioner Azure Fundamentals
• Security:Knowledge of Bayer enterprise security standards data residency requirements regulations and AI model governance.
• Certifications:Azure Solutions Architect or Azure AI Engineer certification

Ever feel burnt out by bureaucracy Us too. Thats why were changing the way we work- for higher productivity faster innovation and better results. We call it Dynamic Shared Ownership (DSO). Learn more about what DSO will mean for you in your new role here

Bayer does not charge any fees whatsoever for recruitment process. Please do not entertain such demand for payment by any individuals / entities in connection with recruitment with any Bayer Group entity(ies) worldwide under any pretext.

Please dont rely upon any unsolicited email from email addresses not ending with domain name or job advertisements referring you to an email address that does not end with . For checking the authenticity of such emails or advertisement you may approach us at