Top Tech Jobs & Startup Jobs

Designs and maintains scalable MLOps infrastructure for aircraft maintenance machine-learning systems. Responsibilities include building ETL/ELT pipelines, deploying and serving models, optimizing compute resources, implementing monitoring and CI/CD, and collaborating with data scientists and maintenance engineers. The role supports predictive maintenance, fault detection, anomaly detection, and component lifecycle optimization while mentoring engineers and integrating technologies across distributed systems.

Designs, builds, and maintains APIs, backend services, and large-scale systems. Collaborates with engineering teams to develop features and improve engineering standards, tooling, and processes using Java, Kotlin, Spring Boot, AWS, microservices, and multiple database technologies.

Lead product discovery and end-to-end UX/UI design for complex, data-rich digital platforms. Conduct user research and usability testing, create wireframes and prototypes, build scalable Figma design systems, and collaborate with product, engineering, and data science teams to deliver technically viable interfaces and smooth design-to-code handoffs.

Owns the full lifecycle of predictive and optimization models, from exploratory analysis and feature engineering through AWS deployment and monitoring. The role involves statistical modeling, deep learning, experimentation, A/B testing, MLOps, model explainability, and performance optimization. The Senior Data Scientist partners with engineering, product, commercial, and strategy teams to solve business problems and influence marketplace metrics, conversion, and revenue.

Build and maintain scalable full-stack applications using Python and React.js. Responsibilities include developing microservices and APIs, integrating AI/ML models, optimizing databases and performance, implementing authentication and authorization, managing cloud deployments, writing automated tests, monitoring production systems, resolving issues, and documenting technical solutions. The role collaborates with frontend engineers, product managers, and stakeholders to deliver secure, reliable, high-performance software.

Develop and maintain scalable backend microservices and REST/gRPC APIs using TypeScript, Node.js, and Go. Build event-driven workflows, optimize databases and caching systems, deploy applications on AWS and Kubernetes, and support reliable production operations. The role also involves testing, debugging, documentation, code reviews, and collaboration with product and engineering teams to deliver AI-powered property search experiences.

Design, build, and maintain APIs, services, and systems across engineering teams. Collaborate with engineers to develop large-scale features while improving engineering standards, tooling, and processes. The role requires extensive mobile development experience, strong iOS or Android knowledge, and at least two years developing Flutter applications with Dart.

Build and scale Azure-based data pipelines and a shared data foundation for e-commerce search and product discovery. Integrate clickstream, query logs, telemetry, and product interaction data using Medallion Architecture. Implement distributed processing, data quality checks, lineage, governance, and relevance feedback loops. Collaborate with analytics, data science, and product teams while delivering clean, tested, documented code and a functional prototype rapidly.

Develop scalable, responsive web applications with React.js, translating designs into accessible, high-quality interfaces. Responsibilities include frontend architecture, state management, performance optimization, API integration, automated end-to-end and unit testing, code reviews, troubleshooting, documentation, and collaboration with backend developers. The role also requires staying current with frontend technologies and contributing to architectural and development best practices.

Design and maintain scalable ML infrastructure for training, deployment, and real-time or batch serving. Build data pipelines, optimize compute and model performance, implement monitoring and CI/CD, and integrate machine learning models into production. Collaborate with data scientists and stakeholders, research technologies, mentor engineers, and support revenue optimization systems including ranking, recommendations, and pricing.