Q
AI Solutions Engineer
Not specifiedFull-timeAI EngineeringQuantum ComputingComputer Hardware
Build and deploy AI applications, agents, automated workflows and knowledge systems for internal teams and customers. Own solutions from use-case selection through implementation, adoption and ongoing reliability, partnering with IT and DevOps on infrastructure, security and data governance.
Responsibilities
- Build, deploy and maintain AI applications, agents, custom skills and automated workflows.
- Create a knowledge-management system with content ownership, review and refresh processes, deprecation, search, and technical onboarding materials.
- Work across customer success, support, product and other teams to identify and prioritize high-impact use cases for AI.
- Connect business systems, data warehouses, APIs and vector stores to support retrieval and automation.
- Apply software engineering practices to prompts and agents, including versioning, output validation, error handling and monitoring.
- Measure adoption and outcomes, deliver enablement, and create practical guidance for users and builders.
- Partner with IT and DevOps on technical and hosting requirements, security and data governance standards.
Requirements
- At least 3 years of hands-on software, application or data engineering experience, including production solutions you have shipped.
- Strong Python and/or TypeScript/JavaScript skills, including code reading, debugging and auditing.
- Practical experience building with LLMs, including prompt and context design, agents or agentic workflows, and API integrations.
- Working knowledge of SQL, data modeling, REST APIs and webhooks; experience with a data warehouse such as Snowflake.
- Sufficient infrastructure and security knowledge to define requirements and build within IT and DevOps guidelines.
- Fluent written and spoken English.
Nice to have
- Experience with RAG, retrieval systems, vector databases or agent frameworks.
- Familiarity with knowledge-base platforms, business systems and automation tools.
- A degree in Computer Science or Software Engineering.
- Experience in B2B deep tech, hardware or scientific instrumentation.