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Role Description

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.

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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.

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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.
Exposure to MLOps concepts (model registries, experiment tracking, monitoring, automated retraining).
Experience with AI-assisted development and rapid prototyping workflows.
Experience leading projects or technical workstreams, or mentoring junior engineers.
Experience working in regulated industries (e.g., biopharma, healthcare, finance).
A portfolio of successfully launched AI/ML projects across multiple business domains.
A track record across multiple, diverse AI/DT/IT projects is strongly preferred over deep specialization in a single domain.

이러한 여정으로 진행됩니다.

전형절차

서류전형 > 필기전형(SKCT) > 면접전형 > 채용검진/처우협의 > 최종합격

전형절차/일정은 상황에 따라 변동될 수 있으며, 전형 결과에 따라 추가 절차(인터뷰 등)가 진행될 수 있습니다.
채용 과정 중 필요 시, 지원자의 경력 및 평판 확인을 위해 레퍼런스 체크가 진행될 수 있습니다.

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기타사항

국가 보훈 대상자 및 장애인은 관련법에 의거 우대합니다.
석,박사 학위 소지자의 경우 학사를 포함한 전체 학력 정보를 기입해 주시기 바랍니다.
Search Firm과 SK Careers 포털 간 중복 지원은 불가합니다.
당사 채용 공고 및 SK그룹 계열사 채용 공고 간 중복 지원은 불가합니다.
병역필 또는 면제자로서 해외 여행에 결격 사유가 없는 분에 한하여 지원이 가능합니다.

AI Engineer (Experienced Hire)

이런 일을 합니다.

Role Description

이런 분과 함께 하고 싶습니다.

Qualifications

Education

Major

Experience

Required Skills

Domain knowledge

Other skills

English Proficiency

이런 경험이 있다면 더 환영합니다.

Preferred

이러한 여정으로 진행됩니다.

전형절차

서류전형 > 필기전형(SKCT) > 면접전형 > 채용검진/처우협의 > 최종합격

미리 확인해 주세요.

근무지

기타사항

이런 일을 합니다.

Role Description

이런 분과 함께 하고 싶습니다.

Qualifications

Education

Major

Experience

Required Skills

Domain knowledge

Other skills

English Proficiency

이런 경험이 있다면 더 환영합니다.

Preferred

이러한 여정으로 진행됩니다.

전형절차

서류전형 > 필기전형(SKCT) > 면접전형 > 채용검진/처우협의 > 최종합격

미리 확인해 주세요.

근무지

기타사항