Top Tech Jobs & Startup Jobs

The Senior AI/ML Engineer will design and deploy AI systems for business impact, lead MLOps practices, and collaborate with teams to drive AI solutions.

The Backend Developer (.NET) will design, develop, and support backend services, improve quality through automated tests, and collaborate during integration processes.

The Talent Acquisition Specialist will lead full-cycle recruitment across regions, source candidates, manage hiring processes, and enhance candidate experience.

Design, implement, and optimize AI-driven workflows, develop AI agents, monitor systems, and determine automation opportunities in a fast-paced environment.

The AI Automation Engineer will design and implement AI-driven workflows, optimizing business processes and deploying AI solutions across the organization. Responsibilities include partner collaboration, automation design, AI model evaluation, and documentation creation.

Work with product owners, managers, designers and developers to analyze product data, define business requirements, support backlog prioritization, create documentation and process maps, and monitor post-release performance to recommend continuous improvements that enhance user experience and business outcomes.

Lead end-to-end product design across web and mobile: user flows, wireframes, prototypes, visual systems, usability testing, analytics-driven iteration, collaboration with product and engineering, design leadership and mentorship, and driving scalable design systems and AI-enabled design practices.

Lead end-to-end product design for web and mobile: create user flows, wireframes, interactive prototypes, and scalable design systems. Conduct user research and usability testing, collaborate with product and engineering, mentor designers, apply data and AI tools to iterate designs, and ensure consistent, production-ready UX/UI implementations.

Lead design, development, and productionisation of ML models and MLOps pipelines (training, deployment, monitoring, retraining). Deliver AI use cases (CLV, churn, fraud), ensure model governance, explainability (eg. SHAP), collaborate with Data Engineering and stakeholders, and mentor team members to drive measurable business impact.

Lead design, development, and productionisation of end-to-end ML systems (training, deployment, monitoring, retraining). Drive AI use cases (CLV, churn, fraud), build MLOps/CI-CD pipelines, implement monitoring and explainability (e.g., SHAP), enforce model governance, collaborate with Data Engineering and business stakeholders, and mentor team members to deliver measurable business impact in regulated contexts.