At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where ,Health for all, Hunger for none’ is no longer a dream, but a real possibility. We’re 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 you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

Senior AI Engineer 

POSITION PURPOSE:

We are looking for a highly skilled Azure Local Agentic AI Engineer to join our team. In 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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:

Required

• Master’s or Ph.D. degree in Computer Science, Information Technology, Data Science, Artificial Intelligence or a related field or Bachelor’s 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. That's why we're 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

https://www.bayer.com/enfstrategyfstrategy

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 don’t rely upon any unsolicited email from email addresses not ending with domain name “bayer.com” or job advertisements referring you to an email address that does not end with “bayer.com”. For checking the authenticity of such emails or advertisement you may approach us at HRSUPPORT_INDIA@BAYER.COM.