Hybrid Senior AI Engineer

at Organon

• Job title: Senior AI Engineer
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Azure, Cloud, Cyber Security, Java, Python
• Location requirements: Hybrid, Lisbon, Portugal

About the role

• AI Sr. Engineer designing and building Generative AI systems for enterprise use at Organon. Collaborating with cross-functional teams to deliver production-ready AI solutions with a strong user focus.

Responsibilities

• 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.
• Stay current with advancements in GenAI, agentic frameworks, and related technologies—and apply them pragmatically to real‑world needs.
• Document architectures, design patterns, and reusable components to accelerate adoption across teams.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Data Science, OR equivalent practical skills and expertise demonstrated through work experience.
• At least five years hands on experience in software engineering roles.
• 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.
• Experience with RAG architectures, vector databases, and knowledge-grounding techniques (Preferred).
• Exposure to machine learning concepts and model lifecycle practices (Preferred).
• Experience building reusable AI components or platforms for multi-team adoption (Preferred).
• Experience collaborating with infrastructure, security, or compliance teams to ensure adherence to organizational AI standards (Preferred).

Benefits

• Flexible Work Arrangements: Flex Time, Hybrid