AI Engineer

• Own operations problems end-to-end — Understand the operational pain, define the solution, be its PM, architect it, build it, ship it, and iterate until it actually works in production
• Drive AI transformation across the company — introduce AI-first practices into an R&D org with legacy habits; champion new tooling and prove it with shipped results
• Design and deploy agentic systems — AI agents, human-in-the-loop workflows, operational UIs, CRM integrations, n8n/orchestration pipelines that automate ops end-to-end
• Cross boundaries freely — get into other teams' code, other domains, other stacks; no "not my area"
• Work directly with business stakeholders — translate vague operational feedback into precise, well-architected solutions
• Reports to VP, R&D; works directly with business and ops leadership — you set priorities with them, not receive them

AI Transformation Engineer

• Build AI agents that solve real engineering problems across the networking stack — and ship them to daily use.
• Design and build the platforms that make those agents reliable: typed tool surfaces, cloud services, and the systems behind them.
• Work broadly across networking architecture teams to identify high-impact AI use cases and turn them into working solutions.
• Drive rapid and responsible AI adoption across the organization: establish best practices, guardrails, and evaluation methods so teams embrace agentic workflows with confidence.
• Build evaluation pipelines that measure and improve AI-agent behavior on real production workloads.

Senior Software Engineer, AI Agent Platforms

Machine Learning Engineer

• Develop and manage scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks
• Design and implement robust model serving infrastructure using platforms like TorchServe, TensorFlow, Triton etc.
• Develop scalable inference architectures optimized, with ultra-low latency and high throughput
• Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback capabilities
• Develop logging, alerting, and monitoring solutions to track model development, and reliability
• Improve GPU usage, enable autoscaling, and streamline resource allocation to boost efficiency
• Design, implement, and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data

MLOps Engineer

• Lead the development and integration of new features for Red Hat AI & Cloud, utilizing Nvidia’s cutting-edge technologies (GPUs, DPUs, and more)
• Write and optimize code that integrates hardware accelerators into the Red Hat portfolio, ensuring high performance for AI/ML workloads
• Take ownership of project modules, ensuring they meet rigorous non-functional requirements such as security, resiliency, and maintainability
• Partner with UX, UI, and QE teams to build seamless user experiences for our partners and customers
• Maintain high code quality through rigorous peer reviews and automated testing (CI), and proactively address security vulnerabilities
• Coach and guide junior and mid-level engineers, sharing technical knowledge to foster a culture of continuous learning
• Explore and experiment with emerging AI technologies relevant to software development, proactively identifying opportunities to incorporate new AI capabilities into existing workflows and tooling

Senior Software Engineer – Nvidia AI Ecosystem

AI Research Scientist

• Research and develop novel AI-based solutions for image and video understanding, generation, transformation, and optimization.
• Explore and evaluate the latest advances in Generative AI and multimodal models, and identify practical applications for Cloudinary products.
• Train, fine-tune, evaluate, and optimize large-scale AI models and pipelines.
• Design scalable approaches for serving AI models efficiently in production environments handling billions of assets.
• Prototype new ideas rapidly and collaborate with engineering teams to bring research innovations into production.
• Investigate opportunities to create proprietary models, unique workflows, and differentiated AI capabilities.
• Stay up to date with the rapidly evolving AI ecosystem, including open-source and commercial foundation models.
• Collaborate across research, engineering, and product teams to define future AI-driven experiences and capabilities.
• Help shape the technical direction of Cloudinary’s AI initiatives.

Senior AI Researcher

• Own and drive AI engineering maturity journey
• Audit current AI tool usage
• Establish CLAUDE.md-equivalent constitution files
• Drive daily active usage above 70% across engineering org
• Design and deliver hands-on enablement
• Architect and implement agentic infrastructure
• Define and maintain AI engineering maturity roadmap
• Advise engineering leadership on organizational needs

Principal AI Engineering Lead

• Operate & Develop ML/LLM platforms on Kubernetes + cloud (Azure; AWS/GCP ok) with Docker, Terraform, and other relevant tools
• Manage object storage, GPUs, and autoscaling for training & low-latency model serving
• Manage cloud environment, networking, service mesh, secrets, and policies to meet PCI-DSS and data-residency requirements
• Build end-to-end CI/CD for models/agents/MCP tooling (versioning, tests, approvals)
• Deliver real-time fraud/risk scoring & agent signals under strict latency SLOs.
• Maintain MCP servers/clients: tool/resource definitions, versioning, quotas, isolation, access controls
• Integrate agents with microservices, event streams, and rule engines; provide SLAs, tracing, and on-call runbooks
• Measure operational metrics of ML/LLM (latency, throughput, cost, tokens, tool success, safety events)
• Enforce governance: RBAC/ABAC, row-level security, encryption, PII/secrets management, audit trails.
• Partner with DS on packaging (wheels/conda/containers), feature contracts, and reproducible experiments.
• lead incident response and post-mortems.
• Drive FinOps: right-sizing, GPU utilization, batching/caching, budget alerts.

Machine Learning Operations Engineer

• Lead research and prototyping of advanced machine learning methods, such as foundation models, vision-language architectures, diffusion models, image/video generation, self-supervised learning, imitation learning, and reinforcement learning.
• Prototype ML models that improve perception, prediction, or decision-making for autonomous driving.
• Collaborate with cross-functional teams, including perception, robotics, and systems engineering.
• Participate in technical discussions, share insights, and work towards publishing results.

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