AI Engineer

Job Description

Kyberlife is building the next-generation open marketplace for life sciences, pharmaceuticals, and healthcare product trading. As an AI Engineer, you will help design, build, and maintain AI-powered systems — and the full-stack applications that surround them — to improve how buyers, sellers, and internal teams operate across our marketplace ecosystem.

You will work alongside senior engineers and cross-functional teams to implement machine learning, automation, and generative AI solutions, and to build the backend services and user-facing interfaces needed to ship them as real product features. This includes contributing to intelligent search, recommendation systems, document understanding, workflow automation, and generative AI applications — end to end, from data pipeline to API to UI.

We are looking for engineers who combine solid software engineering fundamentals with a practical, hands-on approach to AI, and who are comfortable working across the stack — from model or pipeline code, through backend APIs, to the frontend surfaces that expose them to users.

What you will be doing

Build and maintain AI features that support the buyer experience, such as product search, semantic matching, recommendations, and quotation assistance — including the UI components users interact with.

Help develop and improve machine learning models for use cases like pricing intelligence, demand forecasting, lead scoring, and fraud detection.

Build generative AI components such as internal copilots, automated support tools, document summarisation, and workflow assistants — typically under the guidance of a senior engineer or tech lead for the trickier design calls.

Build NLP pipelines to process unstructured data such as product descriptions, supplier catalogues, RFQs, invoices, certifications, and regulatory documents.

Contribute to computer vision or OCR pipelines for extracting structured information from uploaded documents and scanned files.

Design and build backend APIs and services (gRPC) that expose AI features to internal tools and customer-facing products.

Build and maintain frontend interfaces — dashboards, admin tools, chat/copilot UIs, and internal review or annotation tools — using modern frameworks (e.g., React).

Build and maintain data pipelines and support model-serving infrastructure for training and real-time inference.

Integrate LLMs, vector databases, and retrieval systems into production applications, following established patterns and best practices.

Own a feature end to end where needed — from data/model work, through the API layer, to a working UI — coordinating with dedicated frontend/backend engineers on larger efforts.

Work with product, operations, and commercial teams to understand requirements and prototype solutions for AI opportunities.

Follow and help refine team practices for model evaluation, observability, monitoring, and continuous improvement.

Preferred Qualifications

Solid computer science fundamentals — algorithms, data structures, and software engineering best practices.

Programming experience in Python and/or C#, ideally with some production software development experience.

Working knowledge of ML frameworks such as PyTorch, TensorFlow, or scikit-learn.

Comfort building and consuming gRPC/REST APIs, and basic familiarity with a modern frontend framework (React, Vue, or similar).

Some experience deploying AI systems into production using APIs, containers, and cloud infrastructure.

Familiarity with LLMs, prompt engineering, RAG architectures, embeddings, and vector search — hands-on project experience is a plus, production experience not required.

Understanding of data pipelines, ETL processes, and relational/non-relational databases.

Exposure to AWS Cloud and containerized deployments (Kubernetes experience is a plus, not required).

Awareness of MLOps practices such as CI/CD, experiment tracking, and model versioning.

Ability to break down a scoped business problem into a workable technical solution, spanning both the AI/data layer and the application layer.

Good communication skills in English and comfort working cross-functionally.