Top Tech Jobs & Startup Jobs

Lead the security architecture and implementation for an enterprise AI platform: secure cloud infrastructure, data protection, IAM, tenant isolation, and GenAI workflows. Embed security into CI/CD, IaC, containers, secrets management, and operations. Design secure customer deployment patterns, support enterprise security reviews, and partner on compliance (SOC 2, ISO 27001, SSPA) while personally implementing critical security capabilities and guiding cross-functional engineering teams.

Manage full-cycle recruiting for a high-growth AI startup: intake, sourcing, interviewing, offers, and closing across technical, leadership, and business roles. Partner with hiring managers, build candidate pipelines, track hiring metrics, improve processes, and support employer branding and onboarding initiatives.

Lead product strategy and execution for AI-powered enterprise products. Drive development from zero to one and establish product processes.

The Senior GenAI Engineer will design, develop, and deploy scalable GenAI applications using Python and cloud technologies, primarily on Azure. Responsibilities include leading GenAI development, optimizing large language models, and mentoring junior developers.

This role involves managing executive calendars, coordinating meetings and travel, and overseeing daily office operations in a fast-paced environment.

Develop and test AI-powered software solutions, collaborating with team members and learning about AI tools and frameworks.

The Forward Deployed Engineer will work with enterprise customers to deploy and integrate the AI platform, troubleshoot issues, and enhance operational processes, ensuring successful and scalable implementations.

As a Senior Python Developer, you'll lead the design and deployment of scalable applications, enhance system performance, mentor junior developers, and drive innovation in software development.

Owner of hands-on calibration, configuration, and integration of an enterprise Generative AI platform. Design compliant decision logic, run automated validation and evaluation, integrate customer components, rapidly prototype reusable templates, and contribute to governance and delivery methodology to ensure production-ready, regulatory-compliant deployments.

Design and prototype practical AI methodologies (reasoning, retrieval, agents, verification, learning from feedback), evaluate alternatives, build Python/Azure prototypes and reference implementations, create evaluation benchmarks, and convert validated approaches into product-ready specifications while partnering with Product and Engineering for integration and refinement.