Lead AI Solutions Architect @ Bayer

Required: Educational Background Bachelor's Degree or Master's Degree or Ph. D in Computer Science, Data Science, Artificial Intelligence, or a related field. Technical Skills AI & Machine Learning Expertise: Deep understanding of AI concepts, including machine learning algorithms, natural language processing, and deep learning frameworks (e.g., TensorFlow, PyTorch). System Architecture: Proficiency in designing scalable and robust AI systems, particularly for enterprise applications. Cloud Platforms: Extensive experience with AWS services, particularly in: Containerization (ECS, EKS, Fargate) Serverless computing (AWS Lambda, Step Functions) API management (API Gateway) Model Selection & Optimization: Ability to select appropriate AI models and frameworks based on performance metrics, cost-effectiveness, and specific use cases. Experience Industry Experience: 8+ years of experience in AI/ML roles, with a focus on architecture and system design. Project Management: Experience in leading AI projects and managing cross-functional teams. Multi-Agent Systems: Familiarity with designing and implementing multi-agent workflows and decision-making frameworks. Soft Skills Leadership Skills: Proven ability to lead teams and make high-level architectural decisions. Collaboration: Strong interpersonal skills to collaborate effectively with Data Scientists, ML Engineers, and Cloud Teams. Communication: Excellent verbal and written communication skills to articulate complex AI concepts to non-technical stakeholders. Governance & Compliance Knowledge AI Governance: Understanding of AI ethics, bias detection, model drift, and compliance with regulations. Security Awareness: Knowledge of security best practices in AI model deployment and data handling. Continuous Learning Staying Updated: Commitment to continuous learning in the rapidly evolving field of AI, including obtaining relevant certifications. Preferred: Relevant certifications in cloud architecture (e.g., AWS Certified Solutions Architect). AI/ML certifications from recognized institutions (e.g., Google Cloud Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate). 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. Lead AI Solution Architect  We are looking for a Lead AI Solution Architect  Lead AI Solution Architect has complete oversight of AI Engineering within a platform. Individuals in this role leverage their extensive proficiency of LLMs, machine learning models, natural language processing, computer vision, and other AI technologies to set direction, define strategy and architectural best practices, build organizational capability, and champion the role of AI Engineering in driving business priorities.   ,[Architecting AI-powered systems for enterprise-level use cases., Designing scalable LLM architectures with:, Document processing workflows, Chatbot orchestration, Vector RAG optimization, Selecting the right AI models & frameworks based on use case, performance, and cost., Designing multi-agent AI workflows that scale., Architecting rules-based & AI-based decision-making frameworks., Defining the best orchestrator functionality for multi-agent coordination., Architecting containerized AI services using AWS ECS, EKS, Fargate., Optimizing AI pipelines with AWS Lambda, Step Functions, API Gateway., Implementing secure and scalable AI governance models on AWS, Designing AI monitoring & governance frameworks (bias detection, model drift, compliance)., Overseeing LLM evaluation, security & performance tuning., Managing AI integration with enterprise data pipelines., Making high-level AI architectural decisions for model deployment., Collaborating with Data Scientists, ML Engineers, and Cloud Teams to ensure a scalable AI ecosystem., Defining AI best practices & reusable frameworks for engineering teams.] Requirements: AWS, Machine learning, AI Tools: Agile, Scrum. Additionally: Sport subscription, Private healthcare, Flat structure, Lunch card, International projects, Company car, Canteen, Bike parking, Playroom, Shower, In-house trainings, In-house hack days, Modern office.