Overview
We are seeking a Junior AI Engineer to support research, testing, prototyping, model comparison, technical documentation, and the development of controlled AI Proofs of Concept (PoCs). The role involves facilitating technological exploration without assuming direct responsibility for critical production components.
Your Role and Responsibilities
What You’ll Do
- Research emerging generative AI tools, frameworks, models, libraries, and patterns.
- Create rapid PoCs to validate use cases, technical hypotheses, and implementation alternatives.
- Test commercial, cloud-native, and open-source LLMs based on criteria defined by the senior team.
- Evaluate prompts, compare responses, document findings, and propose improvements.
- Assist in the basic development of agents, tools, RAG workflows, and simple connectors.
- Conduct experiments with embeddings, chunking, reranking, vector stores, and document processing.
- Create notebooks, scripts, internal demos, and reusable technical documentation.
- Maintain repositories of examples, lessons learned, comparisons, and technical guides.
- Assist in preparing materials for technical committees, demos, and validation sessions.
REQUIREMENTS
What This Role Requires
- Basic to intermediate Python skills.
- Basic knowledge of generative AI, LLMs, RAG, and agents.
- Basic knowledge of REST APIs.
- Basic Git proficiency.
- Ability to create scripts, notebooks, demos, and prototypes.
- Strong independent research and technical documentation skills.
- Clear interest in AI, automation, data, and solution architecture.
Basic knowledge of LangChain, LlamaIndex, Google ADK, or similar frameworks. - Basic knowledge of GCP, Docker, and SQL.
- Basic knowledge of embeddings, vector databases, and semantic search.
- Familiarity with document processing, OCR, or information extraction.
- Academic or personal experience with AI projects, notebooks, automation, or chatbots.
Working at Motivus
What Working Here Looks Like
- Guild community: You’re part of the [Data & AI / Backend / Cloud] Guild — a community of practitioners in your domain. Structured learning, peer review, and certifications happen here, not in isolation.
- Outcome accountability: Your work connects directly to a client result. We don’t measure activity — we measure delivery.
- AI in your workflow: Through AI Champions and Guild initiatives, you’ll actively use and shape how AI tools are applied to engineering work — not just hear about it in a townhall.
- Defined career path: Structured progression from individual contributor to technical lead to Guild leadership, with certifications and milestones that are visible, not arbitrary.
