Top Tech Jobs & Startup Jobs

Own the end-to-end performance, security, user experience, integrations, and roadmap of an e-commerce platform. Coordinate vendors, systems integrators, and engineering teams; manage ERP, CRM, and payment gateway integrations; analyze web analytics and conversion funnels; optimize site performance; and ensure platform compliance and scalability.

Own growth and distribution for an established AI product. Lead self-serve funnel optimization, product evangelism, technical content, community and ecosystem development, growth-feature roadmaps, experimentation, and instrumentation. Improve acquisition, activation, retention, conversion, and practitioner advocacy by connecting funnel and community insights to product decisions.

Manage IT infrastructure across Windows, Linux, and macOS environments, including hardware procurement, deployment, asset tracking, system availability, Microsoft 365 administration, Intune, Autopilot, Defender, Exchange Online, SharePoint backup, Entra ID, Active Directory, and endpoint security. Responsibilities include troubleshooting, patching, monitoring, incident response, vendor coordination, documentation, audits, disaster recovery support, and employee onboarding and offboarding.

Contribute to end-to-end product design, including user problem analysis, flow mapping, interface design, visual assets, design reviews, documentation, and handoff preparation. The intern will work with experienced designers on real product and client projects, receive mentorship and feedback, and explore AI-assisted design tools. The role is intended for design students completing academic or industry internships.

Designs scalable enterprise AI services and architectures, establishes AI architecture standards, and embeds security, privacy, compliance, and ethics into solutions. The role leads AI roadmap planning, translates business needs into technical designs, oversees data pipelines and model deployment, performs light coding and technical reviews, and advises leadership. Requires Azure, Python, data science, machine learning, AI/ML platform architecture, and model lifecycle experience.

Lead SAP Business AI and Joule consulting assignments from discovery through delivery shaping. Identify enterprise AI use cases, translate business-process needs into SAP-native solutions, shape architectures across S/4HANA, BTP, Joule, workflows, integrations, and governance, and lead customer workshops. Build roadmaps, pilots, proposals, delivery playbooks, accelerators, and team capabilities while coordinating business stakeholders, SAP consultants, architects, engineers, and leadership.

Lead enterprise integration architecture across SAP S/4HANA, BTP, and third-party systems. Design scalable integration blueprints, establish governance and security standards, define migration roadmaps from SAP PI/PO, and oversee performance, monitoring, and availability. Provide technical leadership, conduct code reviews, mentor developers, and collaborate with business leaders to translate objectives into integration strategies. Deliver complex transformations and high-stakes integration projects using SAP middleware, APIs, event-driven architecture, and automated deployment practices.

Lead end-to-end SAP integration projects using CPI, PI, and PO, including migration from PI to PO and PO to Integration Suite. Configure and develop integrations across cloud and on-premises SAP systems, B2B and EDI scenarios, and applications such as Salesforce, SuccessFactors, Workday, and S/4HANA Cloud. Work with diverse adapters, APIs, mappings, iFlows, and integration tools while supporting migration initiatives and complex integration scenarios.

Develop statistical and machine learning models to analyze and optimize mining operations. Collect, clean, integrate, and model data; create dashboards and visualizations; collaborate with domain experts; automate workflows; and deploy production solutions using Python and modern data tools. The role may lead data scientists and engineers and requires expertise in data engineering, SQL, BI, application logic, testing, and infrastructure, with mining or industrial-process knowledge preferred.

Lead Data Scientist responsible for designing, deploying, validating, and monitoring predictive, deep learning, NLP, multimodal, and generative AI models. The role involves statistical modeling, experimentation, causal inference, optimization, A/B testing, KPI definition, and translating business needs into analytical solutions. Responsibilities also include large-scale data processing, ML pipelines, data preparation, governance, and collaboration with product, engineering, and business stakeholders using Python, Databricks, MLflow, and Azure services.