AI Engineer
Design and develop scalable backend infrastructure for Generative and Agentic AI capabilities. Implement custom SAP AI products using SAP BTP and Azure to drive business value and efficiency.
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Design and develop scalable backend infrastructure for Generative and Agentic AI capabilities. Implement custom SAP AI products using SAP BTP and Azure to drive business value and efficiency.
Design and develop scalable backend infrastructure for Generative and Agentic AI capabilities. Implement custom SAP AI products using SAP BTP and Azure to drive business value and efficiency.
Design and develop scalable backend infrastructure for Generative and Agentic AI capabilities. Implement custom SAP AI products using SAP BTP and Azure to drive business value and efficiency.
The role involves implementing and configuring the DX platform to measure the business value and impact of AI-assisted development tools. You will be responsible for integrating telemetry data, defining success metrics, and developing dashboards to support ROI analysis and scaling strategies.
Lead a team of AI engineers to design, build, and deploy production-grade AI services including LLMs, RAG, and agentic workflows. Own the end-to-end technical architecture, evaluation standards, and roadmap alignment with business stakeholders.
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Design, develop, and optimize accelerated data processing software libraries for GPU and CPU architectures. Collaborate with internal and external partners to define technical roadmaps and provide leadership to library engineering teams.
Develop complex AI solutions including chatbots, service bots, and LLM-based applications using RAG architectures. Collaborate with international teams to design, develop, and prepare AI applications for production in cloud and on-premise environments.
You will own the architecture and delivery of production-grade LLM systems and classical machine learning solutions. Additionally, you will lead technical decisions in client engagements and mentor team members on GenAI and ML best practices.
Develop complex AI solutions, including chatbots and LLM-based applications using RAG, for international clients. Design and implement these systems in both cloud and on-premise environments while collaborating with global teams.
Lead the design and development of shared libraries, Spring Boot starters, and agentic frameworks to enable internal teams to build AI agents. Mentor product teams in Python-based AI development and establish standards for agent orchestration, evaluation, and guardrails.
Design and implement production-ready agentic AI solutions and RAG systems using Python and cloud infrastructure. Own the full lifecycle from architecture and development to deployment, monitoring, and operational maintenance.
You will build and evolve the agent harness by implementing hooks, permission gates, and context compaction to ensure reliable task completion. Additionally, you will architect planner/executor/evaluator pipelines and manage agent memory to support long-horizon, multi-session execution.
Design and implement AI-driven GitLab Duo agents to automate the software delivery lifecycle from Jira requirements to merge requests. Establish governance, security guardrails, and bi-directional communication between development tools and project management systems.
Design, build, and operate a sovereign AI toolchain and Kubernetes environments optimized for AI inference and orchestration in isolated settings. Manage open-source LLM stacks, GPU-enabled environments, and secure platform operations to ensure data sovereignty.
The role involves building and optimizing modern data platforms and developing scalable data pipelines. It also focuses on supporting cloud-based analytics solutions within a dynamic international team.
Lead AI-assisted software engineering activities by designing repeatable SDLC patterns for complex cloud software stacks. Evaluate and operationalize AI development platforms and coding agents while coaching engineers on their safe and effective use.
Analyze OpenStack-derived cloud services and use AI-assisted engineering methods to map dependencies, diagnose issues, and create technical handover documentation. Collaborate with cross-functional teams to validate findings and transform them into actionable engineering backlog items.
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The role involves industrializing AI-assisted engineering workflows by creating reusable patterns for codebase intake, dependency extraction, and documentation generation. You will integrate these AI workflows with existing CI/CD systems, Git platforms, and architecture evidence repositories to ensure auditable and scalable engineering outputs.
The role involves strengthening software handover readiness by utilizing AI-assisted engineering to perform code quality, security validation, and dependency analysis. You will collaborate with cross-functional teams to generate evidence packs and ensure that platform automation and services meet enterprise assurance standards.
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