Ai Deployment Manager Jobs

NVIDIA is looking for a hands-on Solutions Architect Manager to lead a team of GPU, networking & software solution architects and engineers. Do you want to build and lead a group that designs, debugs, and deploys new AI hardware and software technologies into production in customer data centers? As part of the NVIDIA SA organization, you will drive people and technical leadership for end-to-end solutions deployments at some of NVIDIA's most strategic technology customers, while directly contributing to designs and deep-dive debugging and shaping our product roadmap with customer feedback. What you will be doing: Recruit & manage a team of solutions architects, system/network and software engineers focused on large-scale GPU and AI networking deployments. Set priorities, allocate resources, mentor, and ensure high-quality customer delivery across multiple concurrent projects - while remaining directly involved in key technical reviews, design decisions, and critical debug efforts. Provide deep subject-matter expertise in advanced GPU and network systems and serve as the senior technical point of contact for strategic customers. Personally lead and guide complex compute/network configuration and performance debugging, working side-by-side with your team to deliver performant, reliable clusters. Guide your team as they lead network / compute / software architecture discussions, and support server, network, and cluster bring-up, including on-site data center work where needed. Systematically collect and synthesize customer-specific requirements across your portfolio. Partner with GPU/Network Systems Engineering, Product Management, and Sales to influence roadmap priorities and packaging of reference designs and solutions. Demonstrate SME in advanced GPU & network systems and be a trusted technical advisor to NVIDIA's strategic customers. Bring customer-sp

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As a Senior Backend Engineer in GitLab’s Platform Enablement organization, you will help make GitLab easier to deploy, validate, and operate across environments. This team is centered on two major areas: Cloud Native deployment guidance and ephemeral environments. You’ll help shape GitLab’s next generation of self-managed deployment guidance as Reference Architectures evolve toward a model based on deployment patterns, workload characterization, component requirements, topology guidance, and scaling principles. You’ll also help improve production-like ephemeral environments so teams can validate changes e

DataHub is an AI & Data Context Platform adopted by over 3,000 enterprises, including Apple, CVS Health, Netflix, and Visa. Innovated jointly with a thriving open-source community of 13,000+ members, DataHub's metadata graph provides in-depth context of AI and data assets with best-in-class scalability and extensibility. The company's enterprise SaaS offering, DataHub Cloud, delivers a fully managed solution with AI-powered discovery, observability, and governance capabilities. Organizations rely on DataHub solutions to accelerate time-to-value from their data investments, ensure AI system reliability, and implement unified governance, enabling AI & data to work together and bring order to data chaos. About the Role We're seeking an experienced DevOps/ Site Reliability Engineering (SRE) Engineer to join DataHub and drive the reliability, scalability, and operational excellence of our platform offerings. In this role, you'll work on technical initiatives across DataHub Cloud and our emerging enterprise deployment solution, which provides customers with enhanced control and flexibility for running DataHub in their preferred environments. Key Responsibilities Enterprise Platform Development: Partner with product and engineering teams to influence the development of advanced deployment capabilities. Collaborate with cross-functional teams to help build systems for seamless installation, upgrade, and rollback processes across various environments. Influence the design and help implement comprehensive monitoring and health check systems for distributed deployments. Partner with engineering teams to help develop self-healing and automated remediation capabilities. Platform Reliability and Operations: Establish and maintain SLAs/SLOs for both cloud and enterprise offerings. Lead incident response and post-mortem processes to drive continuous improvement. Optimise system performance, capacity planning, and cost efficiency. Work closely with product, engineerin

A World-Changing Company Palantir builds the world’s leading software for data-driven decisions and operations. By bringing the right data to the people who need it, our platforms empower our partners to develop lifesaving drugs, forecast supply chain disruptions, locate missing children, and more. The Role The Palantir platform is deployed in numerous critical mission environments including combat zones and classified networks—from the back of a Humvee to a command post to the cloud. This means operating in multiple cloud environments, on-prem air-gapped networks, and at the edge—at scale. We are looking for Edge Infrastructure Engineers to build, operate, and maintain high-performance, scalable, and reliable services for our production infrastructure. This role demands a deep focus on low-level systems, including the deployment and management of physical bare metal servers in both traditional data centers and edge environments. You will be responsible for physical network engineering and the development of robust infrastructure that ensures performance of the Palantir platform. In addition to ensuring performance and reliability, you will play a critical role in building and scaling new environments in a forward-deployed capacity, including onsite. Edge Infrastructure Engineers combine hardware-level engineering experience with the drive to improve existing systems and the creativity to develop novel solutions for evolving challenges. Our team strives to automate processes wherever possible, using whichever tools are best for the job. We strongly believe in engineering teams being responsible for the operations of their services in production. In this role, you’ll work closely with engineers to advocate for and participate in sensible, scalable systems design, sharing responsibility for diagnosing, resolving, and preventing production issues across our most demanding deployments.

A World-Changing Company Palantir builds the world’s leading software for data-driven decisions and operations. By bringing the right data to the people who need it, our platforms empower our partners to develop lifesaving drugs, forecast supply chain disruptions, locate missing children, and more. The Role The Palantir platform is deployed in numerous critical mission environments including combat zones and classified networks—from the back of a Humvee to a command post to the cloud. This means operating in multiple cloud environments, on-prem air-gapped networks, and at the edge—at scale. We are looking for Edge Infrastructure Engineers to build, operate, and maintain high-performance, scalable, and reliable services for our production infrastructure. This role demands a deep focus on low-level systems, including the deployment and management of physical bare metal servers in both traditional data centers and edge environments. You will be responsible for physical network engineering and the development of robust infrastructure that ensures performance of the Palantir platform. In addition to ensuring performance and reliability, you will play a critical role in building and scaling new environments in a forward-deployed capacity, including onsite. Edge Infrastructure Engineers combine hardware-level engineering experience with the drive to improve existing systems and the creativity to develop novel solutions for evolving challenges. Our team strives to automate processes wherever possible, using whichever tools are best for the job. We strongly believe in engineering teams being responsible for the operations of their services in production. In this role, you’ll work closely with engineers to advocate for and participate in sensible, scalable systems design, sharing responsibility for diagnosing, resolving, and preventing production issues across our most demanding deployments. Core Responsibilities Maintaining availability of cloud & physical Ku

Role Purpose We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native: Hands-on ex

Machine Learning Engineer We’re looking for a Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Experience: Multiple years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native:

Machine Learning Engineer IV – (Computer Vision) We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Strong industry experience in Machine Learning, dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native: Hands-on exper

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. Job Description/ Responsibilities: Designing, developing and maintaining stable and reliable AI/ML Ops platforms / pipelines Minimum experience of 4-6 Years required in AI ML Ops Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time. Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with AWS Bedrock is preferable Resource Optimization: Manage GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference for users Collaboration: Work closely with data scientists, data engineers, and software engineers to bridge the gap between model development and production. Version Control & Governance: Manage versioning for data, code, and models using tools like MLflow. Security & Compliance: Implementing data security measures, ensuring compliance with data governance

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. Job Description/ Responsibilities: Designing, developing and maintaining stable and reliable AI/ML Ops platforms / pipelines Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time. Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with AWS Bedrock is preferable Resource Optimization: Manage GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference for users Collaboration: Work closely with data scientists, data engineers, and software engineers to bridge the gap between model development and production. Version Control & Governance: Manage versioning for data, code, and models using tools like MLflow. Security & Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data Technology Eva

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. Our India Global Capability Center isn't just supporting global operations—we’re leading global innovation. After scaling rapidly into a best-in-class hub, we deliver the product innovation and enterprise capabilities that accelerate our global growth, profitability, and scale. As we expand Smartsheet India, we’re searching for Senior AI/ML Ops Engineers who crave variety and ownership. You’ll have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform. You Will: Designing, Developing and overseeing the strategy and architecture of scalable and reliable AI/ML Ops platforms / pipelines Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time. Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. Our India Global Capability Center isn't just supporting global operations—we’re leading global innovation. After scaling rapidly into a best-in-class hub, we deliver the product innovation and enterprise capabilities that accelerate our global growth, profitability, and scale. As we expand Smartsheet India, we’re searching for Senior AI/ML Ops Engineers who crave variety and ownership. You’ll have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform. You Will: Designing, Developing and overseeing the strategy and architecture of scalable and reliable AI/ML Ops platforms / pipelines Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time. Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with

About the Team OpenAI’s Applications Engineering organization builds and operates the products that bring our cutting-edge research to millions of users and developers worldwide. The Applied Foundations team owns the core product and platform layers that make those experiences possible — from identity & access, to safety to payments & commerce across all of our apps. Our teams span product engineering, infrastructure, and safety, working together to deliver technology that is reliable, secure, and trusted at global scale. About the Role You will be a Senior Android engineer on OpenAI’s Applied Foundations team, building the core mobile experiences that power how users sign up, manage their account, family features, pay for services, stay safe, and interact with OpenAI’s products with confidence. This role is about creating high-quality products as well as reusable Android foundations that product teams across different OpenAI apps depend on to ship quickly while meeting the highest standards for security, reliability, and user trust. You’ll own complex client-side systems spanning UI, networking, local state, payment integrations and Apple platform integrations, and work closely with backend, product, and safety partners to shape the architecture that supports OpenAI’s mobile ecosystem at global scale. You might thrive in this role if you: Have 4+ years of professional software engineering experience. Have a proven track record of building high-quality Android applications in production. Are fluent in Kotlin (and/or Java) and familiar with Android development tools and architecture components. Prioritize performance, security, and user experience in mobile development. Enjoy working cross-functionally to bring ambitious product ideas to life. Care deeply about performance, security, and user experience. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We

About OpenAI OpenAI is an AI research and deployment company committed to ensuring that artificial general intelligence benefits all of humanity. We push the boundaries of AI capabilities while prioritizing safety, responsible deployment, and human needs. Our mission requires a team that represents a diversity of perspectives, voices, and experiences. About the Role We are seeking an experienced and versatile Korea B2B Marketing Lead to drive OpenAI’s enterprise and B2B marketing execution in Korea. This is a critical role for a full-stack B2B marketer with strong Korea market judgment - someone who can work closely with local Country/GTM teams, leverage regional marketing support, manage external partners, and deliver high-impact programs across local customer events, content, pipeline creation, customer proof, and sales enablement. This role will be responsible for shaping and executing integrated B2B marketing initiatives that support enterprise adoption, partner-led growth, developer-adjacent business audiences, and long-term customer trust in Korea. The ideal candidate brings deep experience working with sales-led GTM teams, navigating complex stakeholder environments, and translating global and regional strategy into locally effective execution. Key Responsibilities Lead the development and execution of Korea B2B marketing plans, aligned with global priorities, regional marketing support, and Korea Country/GTM objectives. Drive full-funnel B2B marketing programs across local customer events, field marketing, content, customer marketing, developer marketing, and partner marketing. Partner closely with Korea Country/GTM, Partnerships, Product Marketing, Comms, Events, regional marketing, and external partners to deliver integrated programs that support enterprise adoption and pipeline growth. Translate global B2B narratives and launches into Korea-relevant campaigns, local customer events, customer proof, content, and sales enablement. Establish strong Korea mar

About OpenAI OpenAI is an AI research and deployment company committed to ensuring that artificial general intelligence benefits all of humanity. Our mission requires a team that brings diverse perspectives, strong judgment, and a deep commitment to safe and beneficial deployment. About the role We are seeking an experienced APAC Field Marketing Lead to build and lead OpenAI’s B2B demand-generation engine across the region. Reporting to the APAC B2B Lead, this person will own the regional strategy that turns market opportunity into repeatable, measurable pipeline. This is a senior, hands-on role at the intersection of digital demand generation, field marketing, and regional enablement. You will build scale programs that country teams can localize and deploy, while partnering closely with APAC event teams to make events a more strategic, measurable part of the demand-generation mix. Responsibilities Define and execute APAC B2B demand-generation strategy aligned with GTM, revenue, segment, and pipeline priorities Build scale digital programs across lifecycle, webinars, nurture, paid/owned channels Create regional campaign frameworks, content kits, audience strategies, and localization guidance that help country teams move faster and perform better Partner with country marketers and GTM leaders to identify local pipeline gaps, adapt programs, and prioritize support Lead demand-generation strategy for the APAC event portfolio, including event selection, audience acquisition, sales plays, follow-up, conversion, and ROI Connect events, digital journeys, content, and Sales follow-up into integrated full-funnel programs Partner with Sales, RevOps, and Marketing Ops on funnel health, lead quality, attribution, dashboards, and experimentation Translate global B2B programs into APAC-ready motions and bring regional insights back into global strategy Manage agencies, vendors, budgets, timelines, reporting, and post-program learning loops Establish a clear operating rhythm for p