Senior AI Engineer building and deploying AI/ML solutions in production for a consulting firm. Collaborating with engineering and product teams to deliver scalable AI systems and stay updated on AI advancements.
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
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