SK바이오팜의 AX팀에서 기업 전반의 AI 이니셔티브를 주도할 AI Engineer를 모집합니다. Python 기반의 프로덕션 환경 구축 경험과 LLM, RAG, Agentic AI 등 생성형 AI 기술 역량이 필수입니다. 5~15년의 경력을 보유하고 클라우드 환경에서의 모델 배포 및 운영 경험이 있는 분을 찾습니다. 비즈니스 이해도와 원활한 영어 커뮤니케이션 능력이 중요하며, 다양한 도메인의 문제를 기술적으로 해결하는 역할을 수행합니다.
The AI Engineer will join the AI Transformation (AX) team, driving enterprise-wide AI initiatives in direct partnership with business functions—including Supply Chain Management and Commercial.
The team also partners with Staff and corporate functions, converting manual, step-driven workflows into intelligent, automated services.
This is a hands-on individual contributor role, working with the team lead and business stakeholders to identify practical opportunities across the AX project portfolio.
The engineer will define an appropriate technical approach, and build, deploy, and operate production-ready AI services.
Within this portfolio, the engineer will productionize AI models and prototypes from AI scientists into robust, scalable applications.
The engineer will also build AI-native services such as agentic AI systems, multi-agent workflows, LLM-based applications, and RAG pipelines.
The role carries a high degree of freedom in technology choices, and calls for openness to a wide range of stacks and frameworks, selecting and adapting them to fit the available infrastructure.
Building visibility and observability into pipelines from the outset, and using that instrumentation to monitor, diagnose, and continuously improve the system architecture, is a core part of the work.
Because AX projects span multiple functions and move at different speeds, this role rewards broad, cross-functional experience over deep specialization in a single domain.
Success depends as much on communicating well with non-technical stakeholders and learning unfamiliar technologies by doing as it does on engineering depth.
Staying current with emerging AI and engineering trends is essential, and contributions to shared best practices and reusable templates that accelerate project delivery are encouraged and valued.
이런 분과 함께 하고 싶습니다.
Qualifications
Education
Bachelor's degree or higher.
Major
Computer Science, Engineering, or a related field.
Experience
5 to 15 years of professional experience, including building and deploying machine learning or AI systems in production.
An awarded Ph.D. in a relevant field counts as 5 years toward this requirement (other degrees do not count toward the years requirement).
Required Skills
Strong proficiency in Python, with the ability to work in additional languages as needed.
Hands-on experience with generative AI and LLM ecosystems, including prompt engineering, retrieval-augmented generation (RAG), agentic AI systems, and multi-agent orchestration frameworks.
Experience deploying and managing AI models and services in production—on cloud platforms (AWS or Azure), on-premises servers, or hybrid environments combining both.
Proven experience embedding observability into production pipelines (logging, metrics, tracing, alerting), and using those signals to improve system and infrastructure architecture.
Proficiency in SQL for data analysis and pipeline development.
Solid grounding in software engineering practices: CI/CD, containerization (Docker, Podman), automated testing, and version control.
Ability to code and debug independently, with full responsibility for understanding, validating, and maintaining delivered code, including code produced with AI coding assistants.
Domain knowledge
Ability to quickly learn complex, multi-domain business environments—Commercial, SCM, Marketing, and Staff functions—and connect them to practical technical solutions.
Other skills
Strong strategic thinking and problem-solving, paired with the interpersonal skills to work closely with non-technical stakeholders and translate their needs into AI-enabled services.
A practical, resourceful working style, with the agility to thrive in a fast-paced, startup-like environment.
English Proficiency
Professional-level English communication skills are required.
이런 경험이 있다면 더 환영합니다.
Preferred
Advanced degree (Master's or Ph.D.) in a relevant field.
Experience operating containerized applications using Kubernetes or a comparable orchestration platform.
Understanding of IT infrastructure and enterprise systems integration (APIs, authentication, networking, and security fundamentals).
Experience with workflow orchestration or distributed data processing tools such as Airflow, Dagster, or Spark.
Hands-on experience with modern data warehouses such as Snowflake.
