About Gapstars
Gapstars is a Netherlands-based software development services provider that builds remote, agile teams in Sri Lanka and Portugal for innovative tech companies. Today, we are home to 300+ TechStars and innovative minds, turning scalable software into performance products that shape the future. Our partners are highly ambitious tech companies that are looking to conquer their respective markets.
About the Role
We are looking for a hands-on AI Engineer to help design, build, and scale practical Generative AI systems that deliver real value to customers and internal teams.
This is a solo AI role initially, embedded within a strong, collaborative engineering culture. You will work closely with product managers, software engineers, and stakeholders across customer support, marketing, and technology to bring AI ideas into production — quickly, safely, and measurably. The focus is on applied GenAI: leveraging modern LLMs, APIs, and agentic patterns to build reliable, user-facing systems — not training foundation models from scratch.
What You’ll Work On
Customer Support AI Agent:
Build and evolve an AI agent integrated with Salesforce to handle customer support queries, using robust orchestration, tooling, and evaluation frameworks.
AI for Marketing Enablement:
Support the marketing team with AI-powered image editing and content workflows through prompt engineering, automation, and systematic evaluation.
Internal AI Enablement:
Drive AI adoption across the wider engineering organisation by identifying opportunities, building prototypes, and productionising successful use cases.
Responsibilities
Design, build, and deploy production-grade AI systems using modern LLM APIs and frameworks.
Implement agentic AI patterns such as tool use, planning, reflection loops, and multi-step workflows — choosing the right level of complexity for each use case.
Build evaluation frameworks (“evals”) to measure system quality, reliability, latency, UX, and safety.
Develop and optimise RAG pipelines and context engineering strategies where appropriate — without defaulting to RAG as the solution to every problem.
Orchestrate AI workflows that integrate with existing systems (e.g. Salesforce, internal APIs, marketing tools).
Collaborate closely with software engineers to ensure AI systems follow strong engineering practices and are production-ready.
Own AI solutions end-to-end: from experimentation and prototyping through to deployment, monitoring, and iteration.
Apply infrastructure-as-code, CI/CD, and observability principles to AI systems.
Act as an AI experimentalist — staying current with the rapidly evolving GenAI ecosystem and validating new ideas through hands-on experimentation.
Strong software engineering background with production experience building user-facing systems.
Proven experience working with LLM APIs (OpenAI, Anthropic, or similar).
Deep understanding of modern applied GenAI concepts, including:
- Agent orchestration
- Multi-step reasoning workflows
- Tool use and function calling
- Evaluation frameworks and quality metrics
Experience designing and running AI evals beyond basic accuracy checks.
Practical experience with RAG systems, vector databases, and retrieval strategies.
Strong fundamentals in:
- Python
- API design
- Version control
- Testing
- CI/CD pipelines
- Infrastructure-as-code
Ability to move fast, experiment, and iterate — while still delivering robust, maintainable systems.
Excellent communication skills and the ability to explain AI concepts to non-AI stakeholders.
Mindset We’re Looking For
An experimentalist who builds, tests, and learns continuously.
A builder, not a theorist — focused on shipping real products.
Pragmatic and outcome-driven: chooses simple solutions when they work.
Curious and self-driven, often exploring new AI tools and techniques through personal or side projects.
Comfortable being the first AI hire, helping shape how AI is used across the organisation.
