Senior AI Platform Engineer
Design and build reusable platform capabilities for LLM applications, AI agents, and RAG workflows. Develop secure integrations and establish standards for AI deployment, observability, and lifecycle management.
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Design and build reusable platform capabilities for LLM applications, AI agents, and RAG workflows. Develop secure integrations and establish standards for AI deployment, observability, and lifecycle management.
You will embed security by design across AI use cases, agents, and platform components while implementing controls for access, monitoring, and sandboxing. Additionally, you will drive risk remediation, manage AI-specific threats, and strengthen incident readiness through playbooks and cross-team collaboration.
Drive the adoption of AI-assisted development by coaching engineers and building internal AI integrations and tooling. Measure productivity gains through data-backed metrics and establish best-practice frameworks across the engineering organization.
Design realistic AI training tasks and reference solutions based on complex structural engineering concepts. Review AI-generated engineering solutions for technical accuracy and compliance with design codes.
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Own the end-to-end product lifecycle from framing and prototyping to production for AI-native security products. Focus on rapid iteration cycles and direct customer feedback to ship working software quickly.
Build and deploy end-to-end AI-first features for a benefits administration platform using a full-stack approach. Collaborate with cross-functional teams to iterate on prototypes and maintain clean, scalable production code.
Lead the design and implementation of agentic pipelines and LLM-based applications while taking end-to-end responsibility from architecture to production. Define evaluation frameworks and feedback loops to ensure high-quality AI system performance and reliability.
You will build production-ready LLM-based applications and agentic workflows while integrating AI capabilities into existing architectures. Additionally, you will contribute to evaluation and monitoring practices to ensure the reliability and performance of AI systems.
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.
Develop and integrate AI, machine learning, and robotics methods for logistics and supply chain applications. Focus on creating algorithms for vision-based decision-making, motion control, and object manipulation in dynamic environments.
The role focuses on driving the adoption of AI tools and building production-grade automations to optimize operational workflows. It involves collaborating with business stakeholders to identify use cases and ensuring all deployments meet safety and governance standards.
Design and deploy sophisticated agentic AI workflows and RAG pipelines to combat risk and fraud on the Tiko platform. Implement robust traditional machine learning models and production-quality Python code to ensure scalable and secure architectures.
You will be responsible for strengthening the team by managing AI infrastructure and ML production environments. This includes optimizing AI inference, handling GPU workloads, and maintaining scalable real-time AI systems.
The role involves designing, implementing, and optimizing ETL processes to enhance data flow and operational efficiency while monitoring workflows for reliability and performance. Key duties include developing scalable data pipelines using Python, managing Azure storage solutions, and supporting data architecture for AI initiatives.
The role involves refining GenAI user stories and aligning the GenAI scope with the Enterprise Architecture team. The engineer will assess the feasibility, risks, and security implications of GenAI implementations while acting as a technical partner for decision-making.
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Architect and build a toolchain that integrates traditional automation with AI-native agentic workflows to drive company-wide IT transformation. Mentor teams on automation practices and scale the platform to enable self-sustaining cultural change across the organization.
The AI Engineering Lead will design, build, and scale intelligent solutions and automated workflows to improve operational efficiency and farmer impact. They will also lead the full lifecycle of AI-enabled projects while mentoring a team of engineers to drive organizational adoption of new technologies.
Lead the design, development, and scaling of AI-enabled solutions and automated workflows to improve operational efficiency and farmer impact. Drive the organizational AI strategy while mentoring a high-performing team of engineers to maintain high technical standards.
Host live sessions and office hours to teach experienced engineers about system architecture and design tradeoffs. Review student projects and lead mock architecture review boards to provide senior-level technical critique.
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