Jobs at Astreya

Lead requirements gathering, stakeholder engagement, and business process analysis for a cloud replacement program. Produce business, functional, and non-functional requirements, user stories, acceptance criteria, process flows, and support UAT, solution validation, production readiness, and change management across cloud, web, and AI workstreams.

Lead end-to-end utility-scale OSP/ISP fiber infrastructure projects, manage schedules, budgets, and cross-functional teams, review engineering drawings, perform site visits, mitigate risks, drive waterfall and agile project practices, mentor junior PMs, and ensure regulatory and organizational compliance.

Manage and architect global ProjectWise and Egnyte environments, automate workflows with PowerShell, integrate and troubleshoot Autodesk products, execute upgrades and migrations, provide Tier 3 support, enforce governance, and deliver UAT and training to AEC teams.

The role involves managing large-scale AI infrastructure projects, focusing on deployments, risk management, and cross-functional coordination while ensuring alignment with business objectives.

The AI Infrastructure Design Engineer II develops and maintains data hall designs, performs assessments, collaborates with teams, and mentors junior engineers.

The engineer supports simple data hall designs, drafts AutoCAD drawings, assists with capacity planning, and learns data center principles under supervision.

Lead large-scale AI infrastructure deployment programs, manage project execution for GPU clusters and data center infrastructure, and mentor junior project managers.

Perform contract processing and quality reviews to ensure timely, accurate execution. Maintain contract and audit files, prepare amendments and change notices, track payments/deadlines, monitor contractor performance, assist with proposals, analyze and mitigate risk, and manage related administrative tasks and data entry.

Design and optimize AI-ready data models and feature-ready datasets for ML and analytics. Build semantic/analytical layers, enable real-time pipelines, support MLOps, model high-volume time-series/IoT data, and implement data lineage, observability, and governance for lakehouse architectures.