Top Tech Jobs & Startup Jobs

Strengthen application security and DevSecOps practices by deploying SAST, DAST, and IAST tools, conducting threat modeling, integrating security controls into CI/CD pipelines, prioritizing vulnerability remediation, developing security automation, and improving security metrics and documentation. The role partners closely with engineering teams and requires expertise in cloud application security, APIs, authentication, secrets management, infrastructure as code, and modern software development.

Strengthen application security and DevSecOps practices by deploying and tuning SAST, DAST, and IAST tools; conducting threat modeling; integrating security controls into CI/CD pipelines; interpreting vulnerabilities; developing security automation; and partnering with engineering teams to prioritize remediation and improve security posture.

Build and maintain full-stack features for a production healthcare platform using React, Next.js, TypeScript, Node.js, PostgreSQL, and RPC-based services. Contribute to AI and LLM-adjacent systems, handle sensitive regulated data, and collaborate with product, QA, and design teams in an Agile environment.

Build and maintain full-stack features for a production healthcare platform using React, Next.js, TypeScript, Node.js, PostgreSQL, and RPC services. Contribute to AI and LLM-related codebases, handle sensitive regulated data, and collaborate with product, QA, and design teams in an Agile environment.

Author and quality-assure AI-driven automations for defined healthcare workflows, execute accurate high-volume repetitive tasks, provide technical support, and maintain structured application and process documentation. The role contributes to improving core applications and internal tooling while collaborating remotely with product, engineering, and QA teams through GitHub, Slack, and JIRA. This is a three-month remote contract requiring rapid onboarding, strong communication, software engineering experience, and exceptional attention to detail.

Deploy and operate computer vision models across cloud and edge environments. Build reproducible data, training, CI/CD, monitoring, experiment-tracking, and model-versioning pipelines. Optimize GPU workloads for latency, throughput, batching, memory usage, scalability, and cost. Manage model and data drift detection, production reliability, and large-scale image/video workflows while collaborating with data scientists and AI researchers.

Build and operate production MLOps infrastructure for computer vision models across cloud and edge environments. Responsibilities include CI/CD pipelines, dataset and model versioning, experiment tracking, monitoring, drift detection, GPU optimization, and scalable deployment. The role partners with data scientists to productionize image and video models while optimizing latency, throughput, batching, memory usage, reliability, and cost.

Build and operate production MLOps infrastructure for computer vision models across cloud and edge environments. Responsibilities include creating reproducible CI/CD pipelines, managing datasets and model versioning, monitoring system performance and model drift, optimizing GPU workloads for latency and throughput, and deploying image and video models. Collaborate with data scientists and AI researchers to transition experimental models into scalable, reliable production systems.

Build and operate production MLOps infrastructure for computer vision models across cloud and edge GPU environments. Responsibilities include CI/CD pipelines, data ingestion and versioning, experiment tracking, model registries, monitoring, drift detection, GPU optimization, and lifecycle management. The engineer will collaborate with data scientists to deploy reliable, scalable, and cost-effective image and video solutions using Docker, Kubernetes, and cloud platforms.

Build and operate production MLOps infrastructure for computer vision models across cloud and edge environments. Responsibilities include CI/CD pipelines, dataset and model versioning, experiment tracking, monitoring, drift detection, GPU optimization, and scalable deployment. Collaborate with data scientists and AI researchers to productionize image and video workloads while optimizing latency, throughput, batching, memory utilization, reliability, and cost.