AI Engineer 경력 구성원 영입

About the job\nRole Description\nThe 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.\nThe team also partners with Staff and corporate functions, converting manual, step-driven workflows into intelligent, automated services.\nThis is a hands-on individual contributor role, working with the team lead and business stakeholders to identify practical opportunities across the AX project portfolio.\nThe engineer will define an appropriate technical approach, and build, deploy, and operate production-ready AI services.\nWithin this portfolio, the engineer will productionize AI models and prototypes from AI scientists into robust, scalable applications.\nThe engineer will also build AI-native services such as agentic AI systems, multi-agent workflows, LLM-based applications, and RAG pipelines.\nThe 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.\nBuilding 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.\nBecause AX projects span multiple functions and move at different speeds, this role rewards broad, cross-functional experience over deep specialization in a single domain.\nSuccess depends as much on communicating well with non-technical stakeholders and learning unfamiliar technologies by doing as it does on engineering depth.\nStaying 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.\nWho We're Looking For\nQualifications\nEducation\nBachelor's degree or higher.\nMajor\nComputer Science, Engineering, or a related field.\nExperience\n5 to 15 years of professional experience, including building and deploying machine learning or AI systems in production.\nAn awarded Ph.D. in a relevant field counts as 5 years toward this requirement (other degrees do not count toward the years requirement).\nRequired Skills\nStrong proficiency in Python, with the ability to work in additional languages as needed.\nHands-on experience with generative AI and LLM ecosystems, including prompt engineering, retrieval-augmented generation (RAG), agentic AI systems, and multi-agent orchestration frameworks.\nExperience deploying and managing AI models and services in production—on cloud platforms (AWS or Azure), on-premises servers, or hybrid environments combining both.\nProven experience embedding observability into production pipelines (logging, metrics, tracing, alerting), and using those signals to improve system and infrastructure architecture.\nProficiency in SQL for data analysis and pipeline development.\nSolid grounding in software engineering practices: CI/CD, containerization (Docker, Podman), automated testing, and version control.\nAbility to code and debug independently, with full responsibility for understanding, validating, and maintaining delivered code, including code produced with AI coding assistants.\nDomain knowledge\nAbility to quickly learn complex, multi-domain business environments—Commercial, SCM, Marketing, and Staff functions—and connect them to practical technical solutions.\nOther skills\nStrong 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.\nA practical, resourceful working style, with the agility to thrive in a fast-paced, startup-like environment.\nEnglish Proficiency\nProfessional-level English communication skills are required.\nPreferred Qualifications\nPreferred\nAdvanced degree (Master's or Ph.D.) in a relevant field.\nExperience operating containerized applications using Kubernetes or a comparable orchestration platform.\nUnderstanding of IT infrastructure and enterprise systems integration (APIs, authentication, networking, and security fundamentals).\nExperience with workflow orchestration or distributed data processing tools such as Airflow, Dagster, or Spark.\nHands-on experience with modern data warehouses such as Snowflake.\nExposure to MLOps concepts (model registries, experiment tracking, monitoring, automated retraining).\nExperience with AI-assisted development and rapid prototyping workflows.\nExperience leading projects or technical workstreams, or mentoring junior engineers.\nExperience working in regulated industries (e.g., biopharma, healthcare, finance).\nA portfolio of successfully launched AI/ML projects across multiple business domains.\nA track record across multiple, diverse AI/DT/IT projects is strongly preferred over deep specialization in a single domain.\nRecruiting Process\n전형절차\n서류전형 > 필기전형(SKCT) > 면접전형 > 채용검진/처우협의 > 최종합격\n전형절차/일정은 상황에 따라 변동될 수 있으며, 전형 결과에 따라 추가 절차(인터뷰 등)가 진행될 수 있습니다.\n채용 과정 중 필요 시, 지원자의 경력 및 평판 확인을 위해 레퍼런스 체크가 진행될 수 있습니다.\nPlease Read Before Applying\n근무지\n경기도 판교\n기타사항\n국가 보훈 대상자 및 장애인은 관련법에 의거 우대합니다.\n석,박사 학위 소지자의 경우 학사를 포함한 전체 학력 정보를 기입해 주시기 바랍니다.\nSearch Firm과 SK Careers 포털 간 중복 지원은 불가합니다.\n당사 채용 공고 및 SK그룹 계열사 채용 공고 간 중복 지원은 불가합니다.\n병역필 또는 면제자로서 해외 여행에 결격 사유가 없는 분에 한하여 지원이 가능합니다.\nSee more jobs from\nthe same company\nView more\nSee more similar job openings\nView more\nSee more jobs in the same region\nView more