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The QA Automation Engineer will design and execute automated testing strategies for AI-native applications, focusing on API and UI automation. The role involves validating AI-powered workflows and autonomous agents using tools like Playwright, JavaScript, and TypeScript.
Leads Fetch’s end-to-end security program: AppSec, incident response, compliance, and AI governance while building a scalable, auditable security framework and managing a security engineering team.
Develops and validates performance models for AI/ML applications on Apple Silicon, analyzing hardware-software interactions to optimize future hardware architectures and influence next-gen AI/ML systems.
Build production AI agents for healthcare using Snowflake Cortex, Python, and Azure OpenAI while ensuring data governance and privacy.
Designs and implements CI/CD pipelines, automation, and DevOps best practices for cloud/hybrid environments (Azure, OpenShift, Kubernetes) to support eCommerce, AI, and data platforms, collaborating with dev, infra, and security teams.
Build and deploy AI agents in Azure, develop RAG pipelines, and define evaluation metrics for LLM-driven solutions in a greenfield project.
Builds and deploys full-stack applications for an AI platform, using Python/Django for backend services and modern cloud/container tech (Kubernetes, AWS, Docker) to create scalable, reliable software.
Build GenAI-powered applications in production, using AWS tools like Bedrock, Lambda, and DynamoDB, while also training and enabling product teams.
Lead cross-functional programs to build and scale the ML and robotics platforms that power autonomous farming and construction machines, coordinating engineering teams and driving high-impact technical initiatives.
Full Stack Developer building and deploying an AI-powered interactive training platform for customer service teams using Node, NextJS, MongoDB, tRPC, TailwindCSS, and Docker.
Design and implement a high-performance, low-power ASIC accelerator for AI inference in autonomous-driving perception modules using Verilog, SoC IP integration, and timing/power optimization.
Build and scale ML feature pipelines in Snowflake/Snowpark and Databricks, productionize batch/real-time inference, and implement MLOps/LLMOps to drive measurable business impact.
Develops and optimizes cloud storage infrastructure for AI workloads, focusing on scalable, high-performance distributed storage solutions with redundancy and fault tolerance.
Player-coach Engineering Manager leading a small team of Applied AI Engineers who embed with enterprise customers to ship production AI systems using Python, TypeScript, and LLM/agent tooling, based in NYC with moderate travel.
Hyperbound is seeking a Machine Learning Engineer to own the full lifecycle of models for their sales-focused agentic platform. You will be responsible for fine-tuning and deploying open-source models, optimizing them for on-device performance, and building robust evaluation frameworks.
Leads Oracle ERP modernization programs using AI-driven multi-agent solutions, bridging technical and business teams to integrate AI into complex enterprise systems for faster, safer transformations.
A senior engineer who resolves complex Spark/ML/AI issues for Databricks’ largest customers, trains teams, and collaborates cross-functionally to deliver technical solutions aligned with business goals.
Data Engineer building scalable ETL/ELT pipelines, data warehouses, and data lakes using Python, SQL, Apache Spark, Airflow, and cloud platforms (AWS/Azure/GCP) to power analytics and AI/ML products.
Data Engineer designing and scaling data infrastructure for analytics and AI products, building robust pipelines with a modern cloud stack and CI/CD practices in Abu Dhabi.
The Agentic DevOps Lead will lead Agentic DevOps initiatives, architecting and operationalizing a reusable DevOps framework for agentic applications across AWS, Azure, and GCP cloud environments, while leading cross-functional teams of engineers and DevOps specialists.
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