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Design, build, and optimize scalable data pipelines and storage on Azure (data lakes/warehouses). Integrate and transform data from databases, APIs, and streaming sources; tune performance; collaborate with data scientists and stakeholders; work in Agile; mentor junior engineers and deliver robust, production-ready solutions with CI/CD practices.

Design, implement, and monitor cloud infrastructure with Azure; manage CI/CD pipelines; build Grafana dashboards and Prometheus metrics; deploy and maintain containerized applications using Docker and Kubernetes; apply IaC with Terraform and follow security processes while collaborating with development teams.

The role involves leading the design and implementation of smart factory architectures, integrating OT and cloud services while ensuring cybersecurity and data flow validation across multiple sites.

Lead the intelligent transformation of operations support functions through AI and data solutions, overseeing hands-on technical delivery and team leadership in AIOps initiatives.

You will design, develop, and maintain server-side logic for web apps, ensuring efficient APIs, database management, and security best practices. Collaboration with front-end teams and conducting thorough testing are key responsibilities.

Design, develop, and maintain Python backend services, RESTful APIs and microservices. Deploy and manage cloud solutions (Azure preferred, GCP acceptable), implement CI/CD and automated testing, operate Docker and Kubernetes in production, collaborate in Agile/Scrum, participate in code reviews and architecture discussions, and optimize scalability and security.

Build and evolve shared, production-grade AI capabilities for construction software: LLM-based experiences, retrieval pipelines, agents, orchestration, evaluation-driven development, and engineering accelerators. Partner with product teams to integrate reusable patterns, apply reliability and governance guardrails, prototype new approaches, and support other engineers through collaboration, reviews, and knowledge sharing.

Lead presales, RFP, requirements analysis, and solution design for supply chain transformation engagements. Act as a business-technology bridge to capture requirements, conduct fit-gap and process analysis, design scalable planning and execution solutions powered by data and AI, and support implementation, UAT, and client stakeholder management.

The Data Scientist will implement GenAI systems, build components, support project delivery, analyze findings, and deliver technical solutions, focusing on NLP and deep learning.

Design and develop scalable backend applications, optimize performance, ensure security, mentor junior developers, and maintain DevOps practices.