Design and build an agentic AI platform to manage renewable energy certificates and automate related workflows. Collaborate across teams to integrate the platform, optimize user onboarding, and iterate based on feedback and metrics.
About the role
- Lead AI Engineer responsible for designing enterprise AI capabilities at Organon. Collaborating with teams to drive impactful AI innovations in a dynamic healthcare environment.
Responsibilities
- Lead and grow a team of AI Engineers, providing technical direction, mentorship, and career development.
- Drive engineering standards, best practices, and reusable patterns for GenAI and agent-based development.
- Prioritize team workstreams based on enterprise value, complexity, and strategic importance.
- Conduct performance management and mentoring to expand the team’s effectiveness.
- Design, build, and deploy Generative AI and agent-based systems for enterprise use cases.
- Develop intelligent agents capable of reasoning, planning, tool use, and autonomous execution.
- Create AI-powered copilots, conversational interfaces, and decision-support solutions using LLMs.
- Apply an enterprise mindset to ensure AI solutions are scalable, secure, governable, and reusable.
- Integrate AI agents with enterprise platforms, APIs, and data sources to enable end-to-end workflows.
- Monitor, optimize, and support AI solutions running in production environments.
- Partner with business, data, and engineering teams to identify and deliver high-value AI opportunities.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.
- At least 7 years experience leading engineering teams or technical programs.
- At least two years leading an engineering team
- Strong proficiency in Python; familiarity with Java or similar languages a plus.
- Experience with GenAI and agent frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
- Practical experience working with LLMs (OpenAI, Azure OpenAI, Llama, Claude, etc.).
- Understanding of security, scalability, cost, and compliance considerations for AI systems.
- Foundational understanding of cybersecurity principles (secure coding, data protection, access control, least privilege).
- Awareness of common AI risks such as data leakage, model misuse, insecure APIs, and supply-chain vulnerabilities, with the ability to implement basic mitigations.
- Working knowledge of cloud environments such as Azure or AWS, particularly their AI/GenAI services.
- Strong problem-solving skills and ability to collaborate within cross-functional teams.
Benefits
- Flexible Work Arrangements: Flex Time, Hybrid
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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