The Position
Join Boehringer Ingelheim's Cyber Intelligence & Security Operations Center (CISOC) as a Senior Data Scientist / AI Engineer and play a key role in shaping the future of AI-driven cybersecurity operations. In this role, you will design, build, and optimize advanced multi-agent AI solutions that support security incident triage, decision-making, and workflow automation.
Working alongside cybersecurity experts, data scientists, and technology teams, you will develop intelligent systems that leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), machine learning, and agent-based architectures to enhance operational efficiency and accelerate incident response. While experience in cybersecurity is beneficial, candidates with strong AI, machine learning, and agentic AI expertise from other domains are encouraged to apply.
Duties & Responsibilities
As a Senior Data Scientist or AI Engineer, you will be helping in designing and developing an advanced multi-agent system to support the triage of security incidents.
- Design and maintain multi-agent architectures for triage and decision support.
- Develop and refine machine learning models to assess and score incidents.
- Integrate and orchestrate various tools and agents to automate and enrich the triage process.
- Collaborate with project teams to ensure effective implementation and alignment with project goals.
- Document system logic, workflows, and findings for stakeholders.
Requirements:
An ideal candidate will have
- Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related field.
- Equivalent professional experience may be considered in lieu of formal education.
- Minimum of 5 years of experience in Data Science, AI Engineering, Machine Learning, or related technical roles.
- Demonstrated success in developing and refining agent-based systems and machine learning models for decision support in enterprise environments.
Technical Capabilities
- Proven experience designing, building, and deploying agent-based AI systems, including orchestration of multiple agents and integration with diverse tools, protocols (e.g., MCP), and data sources.
- Hands-on expertise with:
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Machine Learning and AI-driven decision support systems
- Agent orchestration frameworks and multi-agent architectures.
- Deep understanding of agent toolchains, agent orchestration frameworks, and best practices for integrating and scaling agent-based architectures in enterprise environments.
- Strong proficiency in Python and relevant programming languages for developing, integrating, and automating AI solutions.
- Practical experience with containerization and deployment technologies such as Docker and OpenShift; familiarity with MLflow or similar platforms for model lifecycle management is highly valued.
- Demonstrated ability to operationalize and maintain AI agents, including monitoring, troubleshooting, and continuous improvement in production environments.
- Experience integrating agent-based systems with enterprise protocols and APIs, ensuring robust, secure, and scalable solutions.
- Certifications in Data Science, AI, or Machine Learning (e.g., Azure AI Engineer, AWS Machine Learning, or similar) or any additional credentials in automation, agent-based systems, or related areas are valued.
Additional competencies
- Ability to work effectively with cross-functional teams, including project stakeholders and technical experts.
- Strong written and verbal communication skills to document system designs and present findings to both technical and non-technical audiences.
- Excellent troubleshooting and analytical skills for developing creative solutions to complex challenges.
- Willingness to stay current with industry trends, technologies, and best practices in AI and automation.
- High level of precision in designing, tuning, and evaluating models and agent workflows to ensure reliable outcomes.