Remote Data Jobs • GenAI

As a Data Engineer on the Ecosystem Automation team, you will build AI-first data pipelines to power our internal metric monitoring and workflow automation platform. The infrastructure you build will underpin this system, utilizing agents or other GenAI tools where required to perform a wide range of workflows across the organisation. You will build an organisation-wide data store, index and expose the data, and build infrastructure to power the context engineering solution that agents rely on.

We are seeking an experienced and forward-thinking AI Solution Architect to design, lead, and implement cutting-edge AI/ML solutions across our organization. The ideal candidate will possess a strong mix of technical expertise, strategic thinking, and leadership capabilities to define AI architectures, guide development teams, and ensure scalable and robust deployments that align with business goals.

Join our team as a GenAI Data Scientist and become the architect of our next-generation detection, prevention, and automation strategies and models for enhanced fraud detection and cybersecurity capabilities. You'll build and deploy cutting-edge AI agents using platforms like Azure and Langchain, creating intelligent systems that can autonomously detect anomalous activities. This position offers a unique opportunity to collaborate and lead cross-functional efforts with internal investigations and law enforcement on synchronized fraud-ring takedown operations.

Research, prototype, and productionize generative AI models. Develop scalable GenAI pipelines that generate high-quality content, from product descriptions, reviews, titles, and other product content. Design and evaluate prompt tuning strategies and RAG systems to ensure factual and engaging outputs. Fine-tune foundation models and develop domain-specific adapters using techniques like LoRA, PEFT, and instruction tuning.

Develop robust, scalable ML software for predictive and generative modeling tasks related to genomics data (eg. Interactome, Cell & Tissue modeling). Design and implement ML algorithms to enhance NGS sequencing pipelines. Apply reasoning techniques—including LLMs, Graph Neural Networks, Gen AI models—for extracting insights to advance drug discovery from simulation, omics data, and literature.