Hire an AI Engineer in Sri Lanka
Applied AI is now a production engineering discipline, not a research experiment. If you need someone who can ship reliable LLM features — not just prototype them — here is what to look for and what I bring to the table.
If you're trying to hire an AI Engineer, you're probably past the "let's try ChatGPT" stage and into building something that has to work reliably in production: a support assistant that answers from your docs, an internal tool that searches your data, or a product feature powered by an LLM. That's a different job than prompt experimentation — it's software engineering with AI components.
What "AI Engineering" Actually Means in Production
Applied AI engineering sits at the intersection of backend engineering and machine learning integration. In practice it means:
- Designing retrieval-augmented generation (RAG) pipelines that ground answers in your real data
- Building with LangChain (or comparable orchestration) without over-engineering the chain
- Choosing and tuning a vector store for retrieval quality
- Managing cost, latency, and token budgets across model calls
- Handling hallucination risk with grounding, citations, and evaluation loops
- Wiring AI features into existing product architecture — APIs, auth, queues, observability
A prototype that answers well once is a demo. A system that answers correctly, cites sources, and degrades gracefully under load is a product.
My AI Engineering Stack
- Models: OpenAI API
- Orchestration and retrieval: LangChain, RAG pipelines with embeddings and chunking strategy
- Vector search: FAISS
- Backend and data work: Node.js, Java/Spring Boot, and Python
- Frontend: React/Next.js for the surrounding product
- Infrastructure: AWS and Docker
The right vector store depends on scale, latency and how much infrastructure you want to run. Beyond FAISS, common options include pgvector on PostgreSQL, Pinecone and Qdrant.
Where This Fits Your Roadmap
- Adding an AI-powered assistant or search feature to an existing SaaS product
- Building an internal knowledge-base RAG system over your company's documents
- Turning a proof-of-concept LLM demo into a production-grade, monitored service
- Reducing AI infrastructure cost without sacrificing answer quality
Why Consider a Remote AI Engineer
The engineering talent pool here has grown around modern full-stack and AI-adjacent work, with strong computer-science education and English fluency. Working remotely with teams in the US, UK, Europe and Australia can give you senior engineering at a lower cost basis than hiring locally in those markets, without cutting corners on engineering quality.
Let's Build Your AI Feature
If you're ready to hire an AI Engineer who treats LLM features as production software — with proper architecture, evaluation, and monitoring — let's talk about what you're building.
I'm Sandaruwan Jayasundara — Senior Software Engineer | AI Engineer. Visit my AI engineering services page or get in touch to discuss your project.