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 pragmatic AI Engineer who can bridge solid software engineering practices with the fast-evolving landscape of applied AI. You’ll help design, build, and evaluate AI systems that are reliable, measurable, and actually useful — not just demos.
You’ll work closely with data scientists, software engineers, and product managers to ship AI solutions that make a real impact. You believe in evaluation before hype, automation where it adds leverage, and simple systems that perform well in production in the life sciences industry.
Responsibilities
Develop and deploy AI-powered applications from prototype to production using modern LLM frameworks and APIs.
Design evals and metrics that measure real-world performance (accuracy, latency, UX quality, safety).
Build and optimize Retrieval-Augmented Generation (RAG) pipelines integrating vector databases and LLMs.
Implement data pipelines, retraining workflows, and monitoring tools that keep models current and performant.
Develop and implement context engineering strategies to improve output quality and control hallucination risks.
Collaborate with product, platform, and infrastructure teams to understand business needs and build systems that scale and fail gracefully.
Apply agentic AI patterns (tool use, planning, reflection loops) pragmatically, choosing the right level of complexity for each use case.
Champion good engineering practices: code reviews, testing, observability, CI/CD, reproducibility.
Ensure AI solutions meet security, compliance, and explainability standards.
Requirements
4+ years of experience in data science or software engineering with 2+ years focused on AI engineering.
Proficiency in Python and experience with LLM APIs (OpenAI, Anthropic, or similar services).
Extensive knowledge of large language models (LLMs) and their underlying architectures, capabilities, and applications.
Experience with GenAI frameworks and libraries (LangChain, LlamaIndex, Haystack, or Hugging Face Transformers).
Experience with vector databases (such as Pinecone, FAISS, Weaviate) and RAG pipeline implementation
Proven ability to evaluate AI systems through custom evals, benchmarking, or data curation.
Experience deploying AI solutions through RESTful APIs and web services.
Familiarity with agentic AI concepts (tool-use, planning, multi-step reasoning), with a practical sense for when simpler systems suffice.
Strong grounding in software engineering principles (version control, modular design, testing) and commitment to measurement-driven iteration
Excellent communication skills and the ability to explain complex AI solutions to non-technical stakeholders.
Strong understanding of AI ethics, bias mitigation, and responsible AI practices.
Nice to Haves
Experience with model fine-tuning, knowledge graphs, or multi-modal AI systems
Knowledge of healthcare industry regulations and compliance (HIPAA, GDPR)
Familiarity with AWS services for scalable GenAI deployment and MLOps practices
