Top Tech Jobs & Startup Jobs

Manage end-to-end technical recruitment for Data & AI roles: partner with hiring managers, source candidates, run interviews, track recruiting metrics, and optimize hiring pipelines while ensuring a strong candidate experience.

Design, build, and operate production data pipelines and dbt models to serve ML and analytics workflows. Work with relational, vector, and graph stores, large-scale Spark/PySpark processing, SQL transformations, Python ETL, and ensure data quality via tests, monitoring, logging, and CI/CD. Collaborate with data scientists and maintain infrastructure-as-code for reliable, production-grade data platforms.

Design, build, and operate production-scale data pipelines and multi-model data platforms (relational, vector, graph) to serve AI/BI systems. Own architecture, reliability, observability, and mentoring; implement ETL/ELT with Spark, dbt, cloud infrastructure, CI/CD, and IaC.

Support design and development of ETL pipelines, assist with data migrations and cloud transitions, troubleshoot data processes, and collaborate with architects, developers, and analysts while learning cloud and data engineering tools.

As a Senior MLOps Engineer, you will build and maintain infrastructure for deploying ML models, create automated ML pipelines, ensure system reliability, and enhance collaboration across AI teams.

As a Data Scientist, you'll analyze data sources, build ML pipelines, and contribute to the development of data analytics platforms while leveraging emerging technologies.

Design and implement customer segmentation, feature engineering, cluster validation, explainable models (SHAP), and media planning logic on large payment transaction datasets. Produce business-ready segment narratives, competitive metrics, and documented methodologies to enable clients to act on data-driven monthly media plans.

Execute end-to-end CRM campaigns: audience selection, offer design, execution, monitoring and reporting. Analyze customer data, build segmentation models (primarily using IBM SPSS Modeller), track campaign KPIs, and translate insights into decision-ready summaries while partnering with cross-functional teams.

Design and implement from-scratch Bayesian hierarchical models (PyMC/Stan), propagate uncertainty through model chains, model media dynamics, optimise constrained budgets, establish causal ground truth via experiments and causal methods, detect structural breaks, and document methodology clearly.

The Engineering Manager will lead technical teams in data and AI projects, ensuring high standards in delivery, manage senior engineers, foster collaboration, and drive improvements in engineering practices.