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.
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.
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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.
Own and operate the AI-focused Data Loss Prevention (DLP) platforms to secure enterprise AI adoption and reduce data leakage risks. Automate security workflows and implement AI guardrails across cloud, endpoint, and network environments.
Own and operate the AI Data Loss Prevention (DLP) platform to manage AI-related data risks and prevent leakage. Engineer security controls across cloud, network, and endpoint environments while automating workflows using Python and Azure tools.
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, 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.
Design and implement production-grade GenAI solutions, AI agents, and RAG systems to automate complex business processes. Collaborate with stakeholders to translate business problems into scalable AI products while maintaining evaluation pipelines and guardrails.
Design and implement production-grade GenAI solutions, AI agents, and RAG systems to automate complex business processes. Collaborate with stakeholders to translate business problems into practical AI applications and maintain the underlying automation workflows.
Design and implement production-grade GenAI solutions, AI agents, and RAG systems to automate complex business processes. Collaborate with stakeholders to translate business problems into practical AI applications while maintaining reliability and observability.
The engineer will own the inference backbone for QVAC's local AI stack, focusing on the C++ systems layer to ensure models run fast, reliably, and predictably on user hardware. Responsibilities include porting and enhancing inference engines like llama.cpp and ONNX to run efficiently on edge devices, focusing on runtime stability and performance.
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.
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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.
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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