Job Description

Overview:

Planview has one mission: to build the future of connected work, from ideas to impact.

As the global leader in work and resource management, Planview helps organizations accelerate the achievement of what matters most, supporting our customers from need to speed, from passion to progress, and from overhead to optimization. We provide the industry's most comprehensive solutions designed for strategic planning, portfolio and resource management, Lean and Agile delivery, product portfolio management, capability, and technology management (enterprise architecture), innovation management, and collaborative work and project management. Our connected platform of solutions underpins the business and digital transformations of more than 4,500 customers globally, including 59 of the Fortune 100.

At Planview, our people connections drive our innovation and success. Our global team of 1400+ work remotely and across our offices including Austin, Vancouver, Hod Hasharon, Bangalore, Sydney, and Stockholm. We're proud of our world-class, connected culture built on our shared values, that supports our teams to be successful from anywhere.

Learn more about our portfolio at planview.com, and connect with us on LinkedIn, Instagram, and X.
Responsibilities:
Work with business stakeholders to enable successful AI adoption. Develop connectors leveraging MCP or third-party APIs to enable new integrations. Prioritize and execute integrations with internal and external data platforms. Collaborate with other engineers to expand AI capabilities. Establish and monitor uptime metrics, set up alerts, and follow a proactive maintenance schedule. Oversee operations by deploying, managing, and troubleshooting Docker-based and serverless environments. Work with DevOps engineers to manage and deploy new tools as required.

Qualifications:
Bachelor's degree in Computer Science, Data Science, or related field. 4+ years of experience in infrastructure engineering, data integration, or AI operations. Strong Python coding skills. Experience configuring and scaling infrastructure for large user bases. Proficiency with monitoring tools, alerting systems, and maintenance best practices. Hands-on experience with containerized and serverless deployments. Ability to code connectors using MCP or third-party APIs. Strong troubleshooting and support skills.

Preferred qualifications:

Experience building RAG knowledge bases, MCP Servers, and API integration patterns. Experience leveraging AI (LLMs) to boost productivity and streamline workflows. Exposure to working with business stakeholders to drive AI adoption and feature expansion. Familiarity with MCP server support and resilient feature design. Skilled at working as part of a global, diverse workforce. * AWS Certification is a plus.