Top Tech Jobs & Startup Jobs

Design and run AI-enhanced sourcing campaigns, maintain structured talent pipelines in Google Workspace, conduct market research and competitor mapping, automate repetitive tasks, ensure accurate data and excellent candidate experience, and evolve sourcing strategies using free LLM and automation tools.

Design and build an AI-native HR platform: backend systems (Python/Django/ASGI), integrate LLMs and agent frameworks, implement RAG/vector search, telephony/voice interfaces, and mentor junior developers while ensuring scalable, low-latency production deployments.

Lead research into credential and secret-exposure threats for agentic AI. Perform offensive investigations, prototype exploits, analyze datasets, collaborate with engineering to improve secret validation, and publish 2-3 technical deep-dives or talks per quarter.

Lead cross-functional Demand Gen, Field Marketing, and GTM Engineering to own pipeline and growth. Build ABM at scale, optimize channel efficiency, run event and regional field programs, own GTM tooling and AI-powered programs, translate ARR goals into quarterly marketing plans, and recruit and develop a high-performing team while reporting to the CMO and partnering with Sales and RevOps.

Design, build, and deploy LLM-powered solutions and scalable backend systems. Implement data extraction, semantic search, document analysis, agentic flows, and evaluation frameworks. Continuously evaluate, tune, and improve model performance while collaborating with product, design, and legal teams.

Lead end-to-end product design for a cross-border second‑hand marketplace: define roadmap with PMs and researchers, use data and research to create mobile-first UX, produce high-fidelity UI and specs, maintain scalable design systems, and mentor other designers.

Build and maintain scalable backend services in Java or Kotlin using DDD. Modernize and refactor legacy systems, implement robust testing strategies, participate in mentoring code reviews, and work with messaging, databases, search, and AWS infrastructure to support a large cross-border marketplace.

Lead end-to-end deployments of autonomous LLM-powered agents in enterprise environments. Design RAG pipelines, multi-agent flows, and integrations; translate field feedback into scalable product features; architect production-grade AI solutions; act as technical consultant for clients, ensuring scalable, secure deployments.

Lead end-to-end deployments of autonomous LLM-powered agents for enterprise clients, design RAG pipelines and multi-agent orchestration, build integrations and scalable data pipelines, translate field feedback into product features, and act as a technical consultant to deliver production-ready AI solutions.

Build, iterate, and maintain a production ML platform and pipelines. Support Data Scientists with tooling for deployment and monitoring, define MLOps roadmap and best practices, collaborate with Data Engineering and DevOps, and evaluate/integrate new ML frameworks and real-time inference tools.