Cloud PPM Functional
The role involves implementing and supporting Oracle PPM Cloud modules for clients. Responsibilities include requirement gathering, system configuration, user training, and documentation.
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The role involves implementing and supporting Oracle PPM Cloud modules for clients. Responsibilities include requirement gathering, system configuration, user training, and documentation.
The engineer will develop AI-native infrastructure platforms, including interfaces and agent-driven capabilities for service management. They will also build and maintain secure, efficient frameworks to simplify infrastructure complexity and enhance developer experience.
Design and implement reusable pipelines for extracting, structuring, and indexing information from unstructured documents like PDFs and maps. Develop agentic workflows and APIs to enable downstream applications and AI services to perform semantic retrieval on processed content.
You will own the architecture and vendor relationships for the marketing data stack, defining the event and identity contract. You will also design server-side conversion pipelines and audience syncs while collaborating with marketing, data, and engineering teams.
The Project Development Engineer II will lead energy audits, perform energy modeling, and manage project schedules for facility and energy solutions. They will also act as a subject matter expert to support business development and lead interdisciplinary teams in project delivery.
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The Senior Cloud Security Engineer will lead the design and implementation of secure cloud architectures and guardrails within AWS. They will also secure AI/ML and Generative AI systems while ensuring compliance with healthcare regulations like HIPAA and HITRUST.
Design, build, and scale real-time health data exchange solutions using FHIR standards and cloud-native infrastructure. Manage complex integration projects end-to-end while ensuring security, compliance, and interoperability across diverse healthcare data sources.
The Data Analyst will support audit preparation by extracting, validating, and analyzing high-volume financial and transactional data within Snowflake. They will also take ownership of Python-based data extraction scripts and collaborate with finance and executive stakeholders to ensure data accuracy.
Design and scale resilient, secure financial investment systems that handle high-volume balances, returns, and money movements, while integrating with external providers and meeting regulatory requirements. Lead technical decisions and team development, evolve the investment platform, and collaborate across functions to deliver measurable product improvements.
Analyze Workday worker, position, and organization data and review security configuration to support identity and role readiness. Design and advise on Workday-driven identity provisioning to Microsoft Entra ID and Active Directory, map business process events to lifecycle triggers, and resolve data discrepancies at their source.
The engineer will own the reliability, availability, and incident response for the SaaS platform while managing cloud infrastructure across AWS and Azure. They will also focus on writing production automation and mentoring teammates to foster a culture of safe deployment.
The Business Intelligence Manager will lead a technical team in modernizing data engineering, analytics, and reporting strategies for state financial and HR systems. This role involves architecting robust semantic layers, managing enterprise Power BI dashboards, and ensuring data governance and security compliance.
The Senior RTL Design Engineer will design, implement, and deliver high-quality RTL for complex digital systems by translating architectural specifications into scalable microarchitectures. They will collaborate closely with architecture, verification, and physical design teams to ensure design correctness, performance, and synthesis readiness.
Design, build, and maintain Python-based backend services, APIs, and microservices throughout the full software development lifecycle. Integrate LLMs, vector databases, and third-party services while ensuring application performance, scalability, and reliability.
You will design, build, and maintain production features while serving as the primary security expert for the platform. Responsibilities include threat modeling new features, triaging vulnerabilities, and implementing secure defaults across the engineering team.
Conduct deep exploratory and user-centric testing to identify friction points and inconsistencies in complex workflows. Deliver actionable insights to the product team while maintaining direct communication with clients regarding progress and risks.
Lead end-to-end business intelligence initiatives from requirements discovery through data modeling, development, and deployment. Partner with stakeholders to define metrics and deliver scalable BI solutions while providing technical guidance and mentoring to the team.
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The Cloud Engineer will design, deploy, and manage Azure cloud infrastructure while collaborating with the Cloud Center of Excellence to implement enterprise-wide solutions. They are responsible for automating infrastructure provisioning, ensuring system reliability, and supporting cloud governance and security initiatives.
The role involves utilizing machine learning and advanced statistical techniques to solve complex business problems and analyze large datasets. You will also lead data science initiatives, mentor junior staff, and communicate the value of AI and ML to organizational leadership.
The Applied AI Specialist leads technical discovery workshops to identify and validate high-impact AI use cases for enterprise production. They collaborate with cross-functional teams and ecosystem partners to define technical requirements and ensure seamless handoff to engineering pods.
The consultant will assess, design, and migrate on-premises computational workloads to AWS while optimizing cost governance and automation. They will also facilitate discovery workshops, manage machine image pipelines, and provide technical support during cutover and knowledge transfer.
You will design and build end-to-end AI solutions by partnering with product managers and analysts to address business pain points. This involves creating AI agents, integrations, and workflows while managing the full lifecycle from prototype to production.
The Edge AI Engineer will design, optimize, and deploy machine learning models for resource-constrained edge devices such as mobile and embedded systems. This role involves performing model compression and hardware-aware optimization to ensure reliable AI performance outside of data centers.
The Observability Engineer will design and operate metrics, logging, tracing, and alerting platforms to ensure system reliability. This role involves managing the full observability stack, from collection agents to dashboards, while focusing on operational ROI and actionable insights.
The Dell Boomi Integration Engineer will design, build, and operate enterprise integrations across the full lifecycle using the Boomi AtomSphere platform. They will collaborate with cross-functional teams to translate requirements into engineered solutions while providing mentorship to junior engineers.
The MuleSoft Developer will design, build, and operate enterprise integration solutions on the MuleSoft Anypoint Platform. They will own the full integration lifecycle, including API design, flow development, deployment, and ongoing optimization.
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Design, build, and operate high-performance inference platforms for serving large machine learning models in production. Manage request routing, batching, caching, autoscaling, and GPU utilization while ensuring end-to-end observability.
The developer will deliver configuration, integration, and customization across the Guidewire InsuranceSuite and Guidewire Cloud. They will collaborate with cross-functional partners to translate requirements into engineered solutions while providing mentorship to junior engineers.
The developer will design, build, and operate large-scale data processing pipelines and analytics platforms using the Hadoop ecosystem. They are responsible for ingesting, transforming, and analyzing massive volumes of structured and unstructured data to support enterprise analytics and machine learning workloads.
Architect, deploy, and operate large-scale Apache Kafka and Confluent environments, owning platform sizing, configuration, security, automation, observability, and developer enablement. Partner with application and cross-functional teams to deliver reliable streaming solutions, while contributing through code and design reviews and mentoring junior engineers.
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