Advance internal AI capabilities by owning and improving high ‑ value, production ‑ ready AI solutions that are reliable, maintainable, and integrated into core business workflows
Work at the intersection of LLMs, machine learning, and modern software engineering, and partner closely with AI, Business, and Enterprise Data teams
Build enterprise ‑ grade systems across cloud and enterprise platforms
Translate technical outcomes into clear business value while helping shape long ‑ term AI strategy and deployment across internal and customer ‑ facing experiences
Design, develop, and deploy LLM and ML based AI solutions into production
Build and maintain RAG pipelines, prompt orchestration workflows, and AI-driven automation systems
Develop scalable inference services, APIs, and integration layers
Investigate and resolve complex system and data challenges across AI pipelines, diagnosing root causes and implementing robust solutions
Define and implement evaluation frameworks to assess AI performance, reliability, and business impact
Integrate AI systems with cloud data platforms and enterprise applications
Partner with business stakeholders to translate operational challenges into structured, measurable AI solutions
Contribute to architectural decisions that ensure scalability, maintainability, and clear system boundaries
Uphold strong engineering standards, documentation practices, and reproducibility across AI systems

strong problem ‑ solving skills
intellectual curiosity
builder mindset to break down ambiguous problems and design, prototype, and iterate on AI or software solutions
MSc/BSc with 2-5 yrs of relevant experience, or PhD plus 1-2+ years
however skills are key!
strong Python skills with solid software engineering fundamentals
experience deploying ML/AI systems into production
experience building APIs or service ‑ based architecture
working with LLM ‑ based systems (RAG, prompt orchestration, evaluation frameworks)
experience working with cloud platforms
operating effectively in fast ‑ evolving, ambiguous environments with a strong sense of ownership
balance execution speed with engineering discipline
communicate technical concepts clearly to business stakeholders
experience with enterprise data platforms, semantic modeling, ontology ‑ driven or knowledge ‑ based systems (bonus)
experience integrating AI solutions into operational business workflows (bonus)
familiar with monitoring, observability, and MLOps practices supporting production AI systems (bonus)

Quarterly profit-sharing bonus
Hybrid Work schedule
Team member appreciation events and recognition programs
Volunteer opportunities
Casual dress code
On demand pay options: Access your pay as you earn it, to cover unexpected or even everyday expenses
All the traditional benefits like health insurance, 401k/401k match, employee assistance programs and time away – don’t worry, we’ve got you covered.

Applied AI Engineer

Key skills