Top Tech Jobs & Startup Jobs

Lead and mentor a development team while overseeing Python application and API development. Design scalable services using Flask, FastAPI, or Django; implement Docker-based workflows; conduct code reviews; and resolve complex technical issues. Apply data science, classical machine learning, and LLM techniques to deliver solutions. Collaborate cross-functionally to prioritize work and ensure timely delivery while improving engineering practices and team productivity.

Manage and maintain networks, servers, hardware, software, cloud systems, and user accounts. Provide escalated technical support, troubleshoot complex issues, implement security controls, conduct audits, maintain compliance with ISO 27001 and SOC 2, manage data recovery procedures, and coordinate IT vendors. The role also involves enforcing access policies, improving infrastructure performance, and supervising IT operations.

Lead consultative B2B enterprise sales and partnership development for AI-driven SaaS solutions. Build relationships with C-level stakeholders, manage complex sales cycles and pipelines, generate and nurture leads, negotiate pricing and contracts, and communicate AI product value through ROI-focused storytelling. Collaborate with product, engineering, customer success, and marketing teams while providing customer insights and adapting sales strategies to market needs.

Develop enterprise-grade web applications and data visualizations for a big data analytics platform. Lead front-end development, build prototypes and proof-of-concept applications, establish JavaScript and GUI best practices, and provide technical implementation expertise. Collaborate with internal teams and clients in an Agile/Scrum environment while maintaining expertise in emerging frameworks, web standards, DevOps tools, and cloud technologies.

Develop and deploy deep learning computer vision systems for real-time video analytics. Responsibilities include training and optimizing detection, classification, tracking, and video models; building end-to-end pipelines; deploying models to edge devices; integrating vision outputs with LLMs; monitoring production performance; and collaborating with backend and DevOps teams on containerized, low-latency deployments.