Software Dev AI/ML Co - op developing cloud solutions and AI/ML workflows at Nokia. Work on Container Technology and Kubernetes while collaborating with a global R&D team.
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
Job title
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Tech skills
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