Senior AI Solutions Engineer, Business Transformation

SK Life Science, Inc. is seeking a Senior AI Solutions Engineer to lead AI transformation across Business, Commercial, Supply Chain Management, and Staff functions. The role enables non-technical teams to adopt AI while personally leading the architecture, development, deployment, and operation of advanced generative AI solutions, including LLM applications, RAG pipelines, agentic AI systems, and multi-agent workflows.

Responsibilities:

• The Senior AI Solutions Engineer brings a track record of building and operating AI services directly, and uses that hands-on foundation to lead AI transformation across Business, Commercial, Supply Chain Management, and Staff functions
• As business teams develop their own AI capability, this role guides each individual and department to the right level of AI application for the work in front of them—from everyday use of conversational AI tools, to customized assistants built on them, to no-code and low-code development, and combinations of these
• For each opportunity, the engineer judges what a colleague can build themselves, what enablement or training would get them there, and what warrants a proper engineering project—then reviews the resulting solutions and determines when they are ready to operate
• For a subset of the portfolio, the engineer serves as project leader: defining the technical stack and architecture, and driving delivery hands-on across advanced builds such as LLM-based applications, RAG pipelines, agentic AI systems, and multi-agent workflows
• Demonstrated ability to lead a portfolio of concurrent projects—intake, prioritization, scoping, scheduling, stakeholder alignment, risk escalation, and outcome reporting
• Practical command of the range of AI adoption paths available to non-developers—conversational AI tools, customized assistants, and no-code and low-code development—with the judgment to match each business need to the right approach
• Ability to review solutions built by non-engineers, assess production readiness, and raise them to an operable standard
• Ability to define the technical stack and architecture for a project and drive it through to delivery, working with others where needed
• Strong proficiency in Python, with the ability to design, implement, and debug independently. SQL proficiency sufficient for data querying and transformation
• Hands-on work with prompt engineering, retrieval-augmented generation (RAG), agentic AI systems, and multi-agent orchestration frameworks
• Experience deploying an AI service used regularly by real users to a cloud (AWS or Azure) or on-premises environment and operating it in production, not limited to PoC or prototype stages
• Ability to build quickly with AI coding tools and to validate and refactor the generated code into production-ready form, with full responsibility for understanding, validating, and maintaining every piece of delivered code
• Working command of core software engineering practices: version control, containerization, CI/CD, and automated testing
• Ability to explain the reasoning behind technical judgments in language non-engineers understand, to guide colleagues toward improving their own work, and to say no to an approach constructively while offering a viable alternative
• Ability to quickly learn complex, multi-domain business environments—Commercial, SCM, and Staff functions such as Legal, HR, and Finance—and to work alongside those teams to connect their needs to practical technical solutions
• Willingness to coordinate with Korea-based team members as projects require, including occasional meetings scheduled across time zones
• Experience designing and running AI literacy or technical training programs for non-engineering audiences, and measuring their adoption outcomes
• Experience bringing solutions built by business users with AI or no-code tools into production
• Experience embedding observability (logging, metrics, alerting) into production services and using those signals to improve system architecture
• Experience building and deploying services under enterprise constraints such as firewalls, proxies, and corporate authentication systems; familiarity with SSO, EAI, and API Gateway, and with IT infrastructure fundamentals (APIs, authentication, networking, and security)
• Snowflake access control and data governance design; pipeline scheduling, dependency, and failure-handling design (Snowflake Tasks, dbt, Airflow, and similar); familiarity with MLOps concepts
• Experience reviewing and managing deliverables from external vendors or outsourced partners
• Projects launched and operated across multiple business domains, described with technical stack, scope of ownership, and operational outcomes
• Mentoring junior engineers or leading technical workstreams