We are looking for a proactive and skilled Software Infrastructure Developer with a strong Agentic AI focus to join our team. In this role, you will design and build the infrastructure, tooling, and AI-powered automation that keep our engineering systems reliable, scalable, and intelligent.

You will be responsible for embedding GenAI and agentic capabilities into our internal developer platforms and CI/CD workflows - creating frameworks, libraries, and multi-agent systems that accelerate automation, improve system stability, and unlock new levels of engineering productivity. This is a hands-on role for someone who thrives at the intersection of infrastructure engineering and applied AI.

The ideal candidate brings deep technical expertise in Python (FastAPI) and/or TypeScript, Kubernetes, CI/CD (Jenkins, GitHub Actions), containerization, and cloud infrastructure - combined with hands-on experience building LLM-powered applications, agents, MCP servers, and RAG pipelines.

What you'll be doing:
AI Platform Development: Design and build internal AI platforms, reusable SDKs, libraries, tools, and MCP servers for agentic applications.
Agentic Workflow Development: Develop single- and multi-agent workflows covering planning, tool selection, delegation, state and memory management, execution, retries, fallbacks, error recovery, and human-in-the-loop approval.
AI-Powered Infrastructure & CI/CD: Integrate AI capabilities into Kubernetes infrastructure and Jenkins/GitHub Actions pipelines to improve development, testing, deployment, and operational workflows.
AI Observability & Evaluation: Implement agent tracing, prompt and tool-call logging, latency and error monitoring, token and cost tracking, regression testing, and failure analysis.
AI Security & Governance: Apply guardrails, access controls, auditability, and governance practices to ensure agents operate safely and reliably.
End-to-End Platform Ownership: Own AI platform capabilities from architecture through production and build reusable solutions that accelerate adoption across engineering teams.
Cross-Functional Collaboration: Collaborate with engineering and business teams to identify high-impact AI opportunities and translate them into scalable technical solutions.
AI Research & Innovation: Evaluate emerging AI frameworks, models, and cloud technologies and promote their adoption where they provide measurable value.

Key Responsibilities
We are looking for a passionate Software Developer to join our WAF SaaS Platform team, where we build, evolve, and operate our companys cloud-native WAF SaaS product.
In this role, you will develop new SaaS product capabilities while also building and operating the cloud infrastructure that powers the platform. You will work across backend services, Kubernetes environments, automation, CI/CD, monitoring, and, when needed, frontend components.
We are looking for a strong team player with a high sense of ownership, a get-things-done mindset, and the willingness to contribute across all areas of development - from UI and backend to DevOps and cloud infrastructure.
Major Responsibilities
Develop new features and capabilities for our companys WAF SaaS product, with a focus on backend services, cloud infrastructure, and DevOps-oriented development
Design, build, and maintain cloud-native infrastructure that supports customer environments, production workloads, and global SaaS deployments
Take part in creating and operating new POPs, improving customer environment provisioning, and evolving the platforms scalability and availability
Develop SaaS product capabilities such as certificate management, CDN-related flows, DDoS-related capabilities, onboarding flows, and other customer-facing platform features
Work with cloud services, CI/CD pipelines, monitoring, logging, and automation tools
Take end-to-end ownership of features and infrastructure components, from design and implementation through deployment, observability, support, and production operation
Build and maintain dashboards, alerts, and operational tools, using data and metrics to improve reliability, performance, and product decisions
Collaborate closely with developers, DevOps engineers, product managers, security researchers, support teams, and other stakeholders
Support production environments, investigate customer issues, and understand the real-world impact of features, bugs, and infrastructure changes.

This is a Principal AI Software Engineer role at the heart of our companys AI Engineering group-where algorithms, infrastructure, and products intersect. Youll lead the evolution and scaling of our companys AI architecture, ensuring reliability, efficiency, and impact across global clinical workflows. The role combines technical depth with cross-functional influence, strengthening how our AI systems are built, integrated, and delivered to drive ongoing impact for patients and care teams.
Responsibilities
Lead cross-domain engineering initiatives connecting AI algorithms (CV, NLP, and deep-learning systems), software, and data systems-from design to delivery.
Stay hands-on, contributing to AI-serving (training and inference), integration, and orchestration components while setting high technical standards.
Partner with Algo teams to connect algorithmic innovation with strong system design, ensuring smooth delivery across Product and Engineering.
Guide architectural evolution, ensuring efficiency, consistency, and maintainability across AI workflows.
Communicate direction and priorities clearly, aligning teams around technical goals.
Mentor engineers and promote technical excellence, sound design, and shared best practices.
Foster collaboration and shared ownership, helping maintain a culture that values clarity, curiosity, and technical excellence.
Uphold quality and reliability-including clean code, testing, and CI/CD discipline-while evaluating and adopting new tools that enhance performance and long-term scalability.

The PerfTech team is looking for a talented C++ Software Engineer to help build the next generation of AI-powered developer tools. You will apply strong C++ and software-engineering fundamentals while gaining hands-on experience with agentic workflows, retrieval systems, and AI services.

In this role, you will contribute to Genie, our company-wide AI knowledge and developer-productivity service. You will work across C++ tools and AI services to help engineers find information, understand complex systems, and work more effectively.

What Youll Be Doing:
Develop production-quality C++ components, APIs, and integrations for our AI-powered developer-tools ecosystem.
Build capabilities connecting native C++ tools with Genies retrieval and agentic features.
Contribute to agentic workflows, retrieval systems, ingestion pipelines, MCP tools, APIs, and enterprise integrations.
Build benchmarks and improve retrieval quality, reliability, performance, and resource usage.
Own features from investigation and design through implementation, testing, and delivery.
Collaborate with graphics, software, and hardware teams developing performance-analysis and developer tools.

As a Senior Principal DevOps Engineer, you will serve as a visionary technical leader within the Cortex Cloud DevOps group. You will define the technical strategy and architecture that ensures our massive-scale production services remain highly reliable, exceptionally secure, and performant. You will pioneer the integration of AI-driven capabilities into our daily operations, establishing elite engineering standards and fundamentally transforming the workflows of hundreds of developers through autonomous agents and intelligent procedures.
Your Career
Architectural Vision & Scalability: Design and scale massive, resilient distributed systems and global Kubernetes infrastructure, implementing robust observability and monitoring frameworks.
AI-Driven Transformation: Revolutionize the SDLC by integrating Generative AI, autonomous agents, and LLM-powered workflows into CI/CD and self-healing systems to accelerate developer velocity.
Technical Leadership & IaC: Define architectural standards, lead GitOps/IaC (FluxCD/Terraform) strategies, and mentor Senior/Staff engineers across the R&D organization.
Developer Experience & Efficiency: Build and champion AI-powered platforms that automate troubleshooting and eliminate friction. Partner directly with Engineering Directors, Principal Architects, and Product Management to align infrastructure initiatives with business goals, optimizing for scale, high availability, and multi-million-dollar cost-efficiencies.
Security & Compliance: Embed "Security by Design" principles into the platform architecture to ensure platform integrity without sacrificing delivery speed.

Join our companys AI research group, a cross-functional team of ML engineers, researchers and security experts building the next generation of AI-powered security capabilities. Our mission is to leverage large language models to understand code, configuration, and human language at scale, and to turn this understanding into security AI capabilities which will drive our company AI future security solutions.
We foster a hands-on, research-driven culture where youll work with large-scale data, modern ML infrastructure, and a global product footprint that impacts over 100,000 organizations worldwide.
Key Responsibilities
Your Impact & Responsibilities
As a Senior ML Research Engineer, you will be responsible for the end-to-end lifecycle of large language models: from data definition and curation, through training and evaluation, to providing robust models that can be consumed by product and platform teams.
Own training and fine-tuning of LLMs / seq2seq models: Design and execute training pipelines for transformer-based models (encoder-decoder, decoder-only, retrievalaugmented, etc.), and fine-tune open-source LLMs on our company-specific data (security content, logs, incidents, customer interactions).
Apply advanced LLM training techniques such as instruction tuning, preference / contrastive learning, LoRA / PEFT, continual pre-training, and domain adaptation where appropriate.
Work deeply with data: define data strategies with product, research and domain experts; build and maintain data pipelines for collecting, cleaning, de-duplicating and labeling large-scale text, code and semi-structured data; and design synthetic data generation and augmentation pipelines.
Build robust evaluation and experimentation frameworks: define offline metrics for LLM quality (task-specific accuracy, calibration, hallucination rate, safety, latency and cost); implement automated evaluation suites (benchmarks, regression tests, redteaming scenarios); and track model performance over time.
Scale training and inference: use distributed training frameworks (e.g. DeepSpeed, FSDP, tensor/pipeline parallelism) to efficiently train models on multi-GPU / multi-node clusters, and optimize inference performance and cost with techniques such as quantization, distillation and caching.
Collaborate closely with security researchers and data engineers to turn domain knowledge and threat intelligence into high-value training and evaluation data, and to expose your models through well-defined interfaces to downstream product and platform teams.

Forward Deployed Engineering sits at the frontier of that work. We embed with our most strategic enterprise customers, running on-prem, air-gapped, and heavily regulated environments, and own the technical arc from discovery through production.

Your day might include designing a solution architecture for a high-impact agentic use case, debugging a production routing issue deep in our codebase, deploying a new MCP server with a customer team, or aligning with a CTO on a rollout plan; all in service of outstanding outcomes and rapid time to value for the organizations we work with.

This is a founding function. The first FDEs will define how we conducts field engineering: the standards, playbooks, and technical bar. The work is hands-on, commercially scoped, and high-leverage: youll partner closely with Product, Research, Platform, Security, and GTM, and what you learn in production directly shapes what the core team ships next. If you thrive under ambiguity, make fast and sound decisions when the stakes are high, and get energy from closing the gap between what customers hit in production and what we build next, this role is for you.

In This Role You Will
Own technical delivery across multiple deployments from first prototype to stable production, including core engineering work that unblocks strategic accounts.
Lead deep technical diagnosis and remediation in our codebase and production environments, model routing and gateway behavior, large-PR scaling, multi-pass behavior, performance and reliability issues.
Build customer-specific extensions (MCP servers, agent skills, platform integrations) that deliver immediate value and become durable product capability; upstream them into the main roadmap where appropriate.
Embed closely with customer engineering teams: serve as their senior technical counterpart for hands-on debugging, architecture reviews, and joint implementation.
Scope work, sequence delivery, and remove blockers early; make deliberate trade-offs between scope, speed, and quality to protect delivery timelines.
Partner with Product, Research, and Platform to convert account learnings and eval-driven feedback into roadmap items, specs, and prioritized engineering work.
Codify working patterns into tools, playbooks, runbooks, and reproducible benchmarks that Support and account teams can operate independently and help scale the field engineering function across the organization.

We are looking for a Data Products Engineer to take full ownership of our internal product suite - the full-stack applications, AI agent tools, and automation pipelines that power teams across the entire company every day. You will step into an established ecosystem and take the reins. Your mission is to maintain, evolve, and build the next generation of these internal tools so every department can move faster and make smarter, data-driven decisions.

Reporting directly to the VP of CS & Data, this is a high-autonomy, tech-lead role. You wont just write code; you will make architectural decisions, solve complex domain challenges with smart software, and shape how the entire company interacts with data.

How will your day-to-day look?
Own the Internal Product Suite: Lead the development of our core full-stack applications. You will build and scale the central platforms and dashboards that give Product, CS, Sales, and executive leadership visibility into product usage, account health, business performance and more.
Advance the Internal AI Layer: Design, deploy, and refine custom agentic AI workflows - enabling teams across the organization to query complex B2B and product data naturally and conversationally.
Engineer Smart Automations: Architect workflow automations and custom APIs that pipe downstream data directly into the operational tools our teams use every day.
Bridge Data and Software: Partner seamlessly with Data Ops and Data Analysts to transform raw data models into highly usable, intuitive user interfaces and automated actions.
Run the Infrastructure: Own the end-to-end DevOps lifecycle for our internal applications, managing deployment, containerization, observability, and CI/CD pipelines.
Drive Technical Strategy: Act as the technical anchor for internal tools. You will translate high-level product and business challenges from company leaders into shipped, reliable software.

As a Senior DevOps Engineer, youll help turn agentic AI capabilities for diagnosing and troubleshooting network and GPU infrastructure into secure, scalable, production-ready services. This role stands out through its end-to-end ownership across cloud and customer-managed environments, close partnership with software and AI engineers, and direct influence on the reliability of NVIDIAs AI infrastructure.


What You'll Be Doing:
Own the DevOps, infrastructure, security, release, and reliability lifecycle - from development environments and CI/CD through deployment, production readiness, and sustained operations.
Build and operate Kubernetes environments and Helm-based deployments for a Python, FastAPI, Node.js, and React microservices platform across SaaS and on-premises footprints.
Engineer GitLab CI/CD pipelines with automated testing, container builds, vulnerability scanning, and versioned image and Helm chart publication through JFrog Artifactory.
Automate infrastructure provisioning, configuration, upgrades, and routine operational workflows to accelerate delivery and improve engineering productivity.
Operate PostgreSQL, Temporal workflow services, and S3-compatible object storage with disciplined capacity planning, backups, recovery testing, and safe migrations.
Strengthen release reliability through deployment validation, reduced-downtime strategies, persistent-state protection, and recovery plans for active workflows.
Deliver actionable observability and security using OpenTelemetry, Datadog/Grafana, Langfuse, secrets management, identity integration, TLS, Kubernetes RBAC, network policies, and container hardening.
Partner with software and AI engineers to troubleshoot distributed systems, investigate incidents, define reliability targets, and improve platform performance, resource efficiency, and customer outcomes.