GPU Core Pipeline IP Verification Engineer

NK Securities Research is a leading financial firm that leverages cutting-edge technology and sophisticated algorithms to trade the financial markets. Founded in 2011, we have gained invaluable experience in the field of High-Frequency Trading (HFT) across different asset classes. Role Overview We’re looking for engineers who can take AI work beyond experiments and make it hold up in production. You’ll work closely with quant researchers and infra engineers to build AI systems that actually get used improving research speed and internal tooling without slowing down the core stack. We value engineers who think about trade-offs, test what they build, and care about how things run in production. What You’ll Build Production AI Ship models that meet defined latency and reliability expectation Add monitoring, rollback, and guardrails before anything goes live Optimise inference across CPU/GPU environments when it matters Integration into Real Systems Plug AI into data-heavy workflows without hurting performance Work within existing low-latency architecture instead of fighting it Profile and remove bottlenecks rather than guessing AI for Engineers & Researchers Build tools that genuinely speed up research and development Improve code understanding, review workflows, and internal knowledge retrieval Keep systems auditable and predictable LLM & Retrieval Systems Implement structured RAG and embedding pipelines with validation in place Create safe integration layers between models and internal systems Performance & Standards Track latency, drift, and stability — not just accuracy Build observability into everything you ship Help raise the bar for how AI is engineered here What We’re Looking For Strong Python fundamentals Clear thinking around system design and performance trade-offs Experience deploying AI systems in production (1–5 years is typical) Familiarity with transformers, embeddings, or LLM deployment Nice to have: Exposure to C++ / Rust / Go E

Location Details: India, Remote At GoDaddy the future of work looks different for each team. Some teams work in the office full-time; others have a hybrid arrangement (they work remotely some days and in the office some days) and some work entirely remotely.​ This is a remote position, so you’ll be working remotely from your home. You may occasionally visit a GoDaddy office to meet with your team for events or meetings. Join Our Team... Here at GoDaddy, the ML Engineering (MLE) team exists as the backbone of our machine learning infrastructure, enabling ML scientists and product teams across Domains to ship models to production reliably, efficiently, and at scale. This team owns the full lifecycle of ML systems — from CI/CD pipelines and model serving infrastructure to GPU workload orchestration and observability. Through disciplined engineering practices, thoughtful system design, and close collaboration with ML scientists, data engineers, and product teams, we deliver the platform that powers domain search, pricing, recommendations, and emerging AI experiences for millions of customers worldwide. We are currently looking for an experienced, highly motivated Senior Engineering Manager to lead our ML Engineering team based in India. This is an established team with existing engineers — we expect the candidate to ramp up quickly on our ML infrastructure stack, build strong relationships with the team, and partner with both India-based teams and US-based teams to drive execution and grow the team further. This individual will join us on our journey to build and scale ML infrastructure that serves real-time predictions at low latency, automates model deployment and promotion, and provides the observability and reliability guarantees that production ML systems demand. Become part of a team that bridges the gap between ML research and production engineering — shipping systems that directly impact GoDaddy's core revenue. What you'll get to do... Lead a team o

About the Role Together AI runs one of the largest GPU fleets in the world. The Infra Agent Systems team builds the software systems that power and automate that infrastructure. We develop production AI agents that diagnose hardware failures, investigate incidents, correlate signals across the fleet, and automate operational workflows. Alongside these agents, we build the platform they run on, including knowledge graphs, retrieval systems, orchestration frameworks, and developer tooling. You’ll work across two areas: Infrastructure Agent Systems — Build production AI agents that help operate our GPU fleet by diagnosing failures, investigating incidents, gathering evidence from live systems, and assisting with remediation. These agents are used every day by our infrastructure and datacenter teams through APIs, CLI, dashboards, and Slack. Core Agent Platform — Build the platform that powers these agents, including knowledge graphs, search and retrieval, orchestration, evaluation, and the tooling that enables agents to reason, act, and continuously improve. We’re working on something that hasn’t really been done before: building knowledge graphs and self-improving AI agents that understand, operate, and continuously improve large-scale AI infrastructure. This is an opportunity to work at the intersection of AI agents, distributed systems, infrastructure, and automation , solving challenging engineering problems with real production impact. There’s an enormous amount to build, learn, and shape as we define the future of autonomous infrastructure. responsible for delivering the software but also for operating and supporting it in production. Why this Role You’ll work on two hard problems at the same time: making AI agents trustworthy enough to operate production infrastructure, and building the knowledge, retrieval, and distributed systems that make those agents effective. You’ll have the opportunity to build foundational systems from the ground up, work on infrastructur

About the Role REMOTE IN INDIA We're looking for a software engineer to build the Kubernetes-native control plane that provisions and runs our GPU inference fleet. You'll design a manifest-driven API where the inference team declares what they need, whether that's a cluster, a model deployment, or a capacity change, and our controllers handle the reconciliation, provider/runtime selection, and lifecycle management underneath, so the inference team never has to know or care which specific serving stack, scheduler, or hardware pool is doing the work. You'll also build the systems that keep the fleet efficient, not just running, including defragmentation and rebalancing logic that consolidates scattered workloads back into contiguous capacity, and scheduling/bin-packing improvements that push GPU utilization up without hurting latency. The core value we're after is decoupling the people building on top of the platform from the operational and runtime complexity underneath, while squeezing more usable capacity out of the same hardware. You'll build the controllers, reconciliation loops, and self-service surface (API/CLI, not tickets) that make that decoupling real, plus the event-driven health, remediation, and utilization systems that keep it running and efficient without a human in the loop. Strong candidates have hands-on experience with Kubernetes controller/CRD patterns, have built or operated a platform API that abstracts multiple backends behind one interface, understand GPU scheduling and capacity efficiency (fragmentation, bin-packing, right-sizing), and think about GPU infrastructure as software to be engineered. A product mindset - you've built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship. You build it, you own it. You are not only responsible for delivering the software but also for operating and supporting it in production. Responsibilities Build the provisioning state machine

We are seeking a qualified Senior Software Tools Development Engineer to join our GPU SWQA team. The successful candidate will have strong experience applying AI technologies to automate test cases and a deep understanding of Windows operating systems. Extensive knowledge of GPU, CPU, SoC, x86, and ARM architectures is required, along with expertise in PC I/O architecture and common bus interfaces such as PCIe, USB, and SATA. Familiarity with specifications for general PC architecture components is a plus. What you’ll be doing: Design and implement automated tests incorporating AI technologies for NVIDIA's device driver software and SDKs on windows platforms. Build tools/utility/framework in Python, C# or equivalent which would help automate and optimize the testing workflows in GPU domain. Develop and carry out automated and manual tests, analyze results, identify and report defects. Rigorously drive test automation initiative. Build innovative ways to automate and expand our software testing. Expose defects and constraints; Isolate and debug the issue(s) and find the root cause; Contribute to the solution and drive to closure. Measure code coverage for the software under test, analyze and drive code coverage enhancements. Develop applications and tools that accelerate development and test workflows and write fast, effective, maintainable, reliable and well documented code. Generate and test compatibility across a range of products and interfaces and validate different key software applications across a test matrix designed to test both breadth and depth. Provide peer code reviews including feedback on performance, scalability and correctness. Report test coverage and Go/No-Go status for deliverables, escalate critical issues, and drive them to closure. Participate in root cause analysis and corrective actions to continuo

We are seeking a qualified Software Tools Development Engineer to join our GPU SWQA team. The successful candidate will have strong experience applying AI technologies to automate test cases and a deep understanding of Windows operating systems. Extensive knowledge of GPU, CPU, SoC, x86, and ARM architectures is required, along with expertise in PC I/O architecture and common bus interfaces such as PCIe, USB, and SATA. Familiarity with specifications for general PC architecture components is a plus. What you’ll be doing: Design and implement automated tests incorporating AI technologies for NVIDIA's device driver software and SDKs on windows platforms. Build tools/utility/framework in Python, C# or equivalent which would help automate and optimize the testing workflows in GPU domain. Develop and implement automated and manual tests, analyze results, identify and report defects. Rigorously drive test automation initiative. Build innovative ways to automate and expand our software testing. Expose defects and constraints; Isolate and debug the issue(s) and find the root cause; Contribute to the solution and drive to closure. Measure code coverage for the software under test, analyze and drive code coverage enhancements. Develop applications and tools that accelerate development and test workflows and write fast, effective, maintainable, reliable and well documented code. Generate and test compatibility across a range of products and interfaces and validate different key software applications across a test matrix designed to test both breadth and depth. Provide peer code reviews including feedback on performance, scalability and correctness. Report test coverage and Go/No-Go status for deliverables, escalate critical issues, and drive them to closure. Participate in root cause analysis and corrective actions to continuously im