Find remote programming jobs in Frontend, Backend, DevOps, QA, or as a FullStack or Data engineer. We curate the best remote software development jobs to match your area of expertise. Software Developers design, specify, create, and maintain web and native apps. Remote Software Engineer jobs where people telecommute (work remotely) are on the rise and we can help you to stay ahead of the curve.
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The OSS Engineer will design, build, and operate internal tools, dashboards, and workflow automations to support network operations and cloud solutions. They will unify fragmented operational data into actionable workflows to increase the efficiency and scalability of the Network Operations team.
The Customer Support Consultant will resolve Level 1 and Level 2 customer issues while contributing to knowledge management and automation initiatives. They will partner with engineering and product teams to improve support infrastructure and the overall customer experience.
Design and implement robust, scalable, and secure backend services to solve complex customer problems. Collaborate with product managers and cross-functional teams to ensure smooth feature delivery and address technical debt.
The Workday Extend Architect will lead the scoping, requirements gathering, implementation, and deployment of Workday Extend solutions for customers. They will also act as a subject matter expert to guide, develop, and educate the wider Workday practice team.
This role manages field inventory strategies, including cycle counts, inventory repositioning, and ensuring product availability for clinical needs. The analyst acts as a liaison between field sales teams and internal supply chain departments to optimize inventory accuracy and support product launches.
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Design and implement scalable, secure, and reliable cloud infrastructure solutions. Translate technical requirements into effective architecture while ensuring high availability and cost optimization.
You will establish a data-driven product management system to support Product-Led Growth and optimize the customer journey. This involves building KPI dashboards, analyzing user behavior, and implementing processes to improve feature adoption and retention.
You will design and define ETL processes and data models while managing the automation and validation of data within a Big Data environment. Additionally, you will coordinate deployment processes and collaborate on various Big Data initiatives within the Market Risk department.
You will own the AI and Agentic Engineering domain within the Google Cloud delivery organization, overseeing offshore quality, velocity, and technical outcomes. You will also partner with onshore leads to provide domain expertise while building reusable assets and scaling engineering teams.
You will collaborate with developers, analysts, and testers to create, test, and evaluate new software solutions for the client. Additionally, you will participate in architectural discussions and focus on optimizing processes while ensuring code quality and security.
Build and maintain commerce surfaces including storefronts, checkout, and subscription management while ensuring high performance and scalability. Collaborate with cross-functional teams to instrument monitoring and mentor junior engineers.
You will collaborate with cross-functional teams to design, develop, and maintain scalable backend systems using PHP and MySQL. Additionally, you will participate in code reviews, troubleshoot production systems, and share on-call duties to ensure high availability and performance.
Manage a portfolio of AWS and Azure cloud projects, ensuring delivery aligns with scope, timelines, and quality standards. Coordinate technical teams and stakeholders while overseeing project documentation, risk management, and operational handovers.
Design, develop, and maintain efficient data pipelines while implementing ingestion and transformation processes using PySpark. Manage complex SQL queries in Snowflake and orchestrate workflows using tools like Airflow to ensure data quality and availability.
You will design and build services to integrate the Synthetic Data platform with the Cint Exchange and third-party survey hosting platforms. Additionally, you will own the end-to-end ingestion path, ensuring data quality and reliability while collaborating with AI/ML engineers.
You will design, develop, and maintain web services and APIs while operationalizing AI-based solutions into the backend. You will also collaborate cross-functionally to make high-level architecture decisions and mentor team members.
Develop and enhance high-performance, user-friendly interfaces for an online betting platform using React.js. Collaborate with cross-functional teams to deliver new features, perform code reviews, and optimize application performance.
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You will design, develop, and maintain web services and APIs while operationalizing AI-based solutions into the backend. You will also collaborate cross-functionally to make high-level architecture decisions and mentor team members.
Design and implement scalable test automation frameworks for API, integration, and performance testing across distributed systems. Collaborate with cross-functional teams to build automated quality gates within CI/CD pipelines to ensure high-quality product delivery.
The specialist will design and evolve governed data models while bridging the gap between complex business requirements and technical implementation. They will collaborate with cross-functional teams to ensure data assets are high-quality, reusable, and aligned with organizational standards.
You will design, build, and ship production AI systems, focusing on retrieval, graph, and inference layers to automate complex digital workflows. The role involves managing AI observability, evaluating model performance, and setting technical patterns for other engineering teams to follow.
You will be responsible for designing, implementing, and maintaining high-reliability distributed systems software. Additionally, you will mentor less experienced engineers and evaluate emerging technologies to improve deployment and monitoring processes.
The Business Analyst will own and coordinate GxP validation packages to ensure systems in pharmaceutical and life-sciences environments are audit-ready. They will translate business and regulatory requirements into documentation while ensuring full traceability and compliance throughout the system lifecycle.
The Staff AI Engineer will design, build, and ship production AI systems, focusing on retrieval, graph, and inference layers. They will lead the development of agentic workflows and ensure the observability and evaluation of AI models within the company's data platform.
Design, build, and maintain Python and Node.js microservices and data pipelines while ensuring system reliability and performance. Own features end-to-end, including technical design, deployment, monitoring, and integration of LLM-driven workflows.
You will be responsible for analyzing, designing, and implementing high-reliability systems software while supporting the full product life cycle. Additionally, you will mentor less experienced engineers and evaluate emerging trends in network operations and automation.
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You will own the test strategy for internal engineering platforms and client-facing products by designing and maintaining automated test suites. Additionally, you will identify manual QA processes to automate them and integrate security regression checks into the CI/CD pipeline.
The Engineer III will monitor, troubleshoot, and improve recovery infrastructure to ensure business continuity for customers. They are responsible for executing complex recovery procedures, managing work queues, and mentoring junior engineering staff.
Manage and evolve SQL Server and PostgreSQL database platforms while leading technical migrations to Microsoft Azure. Ensure high availability, performance, security, and scalability through the design and supervision of hybrid and cloud architectures.
Review and evaluate AI-generated outputs by applying content safety policies and expert human judgment. Document decisions clearly and provide accurate feedback to support the training and improvement of AI systems.
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