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FEQ327R208 While candidates in the listed location(s) in Colorado, are encouraged for this role, candidates in other locations will be considered. At Databricks, our customers challenge us with solving some of the world's toughest data problems. Our customers rely on our Solutions Architect team to understand the technologies that power our platform, the broader cloud and technology ecosystems, and how they can all be brought together to solve these problems. Solutions Architects at Databricks lead the adoption of the Databricks Unified Analytics Platform. As a member of our team, you will sharpen your skills on open-source projects such as Apache Spark™, MLflow, and Delta Lake to advocate the Lakehouse paradigm on public cloud providers. You will gain expertise in cloud platforms, data engineering, data analytics, data science, and machine learning. In this customer-facing role, you will identify use cases for Databricks, develop architectures and solutions using our platform, and guide customers through implementations to deliver strategic business value. You will partner with the sales team to build relationships with our customers and establish yourself as a trusted advisor. Databricks' culture thrives on collaboration and teamwork, and you will contribute your knowledge to our teams by participating in our subject matter expert groups. In this role, you will report to the Solutions Architect Leader supporting the specific business segment. If you are a technologist interested in Data and AI, and enjoy prototyping solutions for customers, this role is for you. The impact you will have: Provide technical leadership for customers to evaluate and adopt Data and AI solutions from Databricks Consult on big data architecture, implement proof of concepts for strategic customer projects, data science and machine learning projects, and validate integrations with cloud services and other 3rd party applications Build and present reference architectures, technical guides, a

Glance Glance is an intelligent shopping agent, redefining the commerce experience. Powered by proprietary agentic intelligence and generative AI, Glance delivers a hyper-personalized consumer experience across mobile and TV — shaping the new era of shopping. Glance is operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of global technology leader InMobi, and is backed by Mithril Capital, Google and Jio Platforms. To learn more, visit glance.com. InMobi InMobi Group is a global technology company shaping the future of agentic commerce and advertising. Through its ecosystem of businesses — including InMobi Advertising and flagship consumer platform Glance — InMobi leverages data, machine learning, and generative AI to help brands reach audiences more precisely and consumers discover products more intuitively. Glance, which is pioneering new models of agentic commerce, is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd. InMobi Advertising InMobi Advertising, part of global technology company InMobi, is an agentic advertising platform helping brands and merchants achieve their business outcomes. Through its proprietary intelligence, AI-led solutions, and vast consumer reach — including flagship consumer platform Glance — InMobi Advertising delivers the omnichannel performance defining what's next in advertising and commerce. Glance is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd. To learn more, visit advertising.inmobi.com. Staff Engineer — Ad Monetization Architecture, Glance We're building the ad monetization platform behind Glance - the lock screen and mobile surfaces that 100M+ people across India, Southeast Asia, Japan, Brazil and the US see every day , roughly half of them actively engaging. Every ad request carries real money , a real user's screen and an OEM partner's trust, and is decided in milliseconds on a low-end Android phone. We're

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, norma

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, norma

Opportunity Consultant - Managed Print Services Description - Job Summary • This role is responsible for closely collaborating with pursuit and account teams to comprehend local engagement and contract scopes, providing essential analysis and information. The role takes charge of negotiating commercial terms and business principles during contracting stages, while also facilitating process proposals and ensuring proper business controls. The role participates in project reviews, develops solutions for data analysis, offers consultation for the SOAR process, and establishes strong customer relationships. This role excels in solving complex problems, leading projects, and supporting operational and strategic plans. Responsibilities • Collaborates closely with the pursuit and account teams to understand the scope of local engagements and contracts, thereby providing the essential analysis and information required by these teams. • Negotiates commercial terms and business principles throughout contracting stages on behalf of the business line. • Facilitates process proposals as needed, such as handling the transfer of vacation accruals and benefits. • Ensures appropriate business controls, including order booking, labor tracking, and project cost control. • Participates in project reviews, verifying the status of project finances, HR aspects, and commercial terms and conditions. • Develops solutions for customer data analysis, conducts in-depth analyses of industry and internal win/loss baselines, and documents and validates assumptions for diverse simulation scenarios. • Provides consultation and necessary documentation for the SOAR process, while also offering consulting expertise to the presales and account teams. • Cultivates positive relationships with the customer team

Job Details: Job Description: The Role and Impact Intel is seeking an AI Model Performance Optimization Engineer to help deliver high-performance AI solutions that enable the next generation of client computing experiences. In this role, you will enable, validate, benchmark, optimize, and debug AI models to ensure they run efficiently and accurately across Intel hardware platforms. Working at the intersection of artificial intelligence, machine learning frameworks, software engineering, and hardware acceleration, you will collaborate with engineers across Intel to improve AI model performance, scalability, and adoption by customers and partners worldwide. This is an exciting opportunity for engineers passionate about Generative AI, machine learning systems, model optimization, and performance engineering who want to influence technologies used by millions of people globally. What You Will Do Key responsibilities will include but not limited to: Enable, validate, benchmark, and optimize AI models on Intel platforms. Analyze model performance and identify optimization opportunities. Debug issues across AI frameworks, software stacks, and hardware platforms. Develop and execute tests to ensure model functionality, accuracy, and performance. Collaborate with software, hardware, compiler, and architecture teams to solve technical challenges. Utilize AI-assisted development tools to improve engineering productivity. Contribute code, bug fixes, documentation, and test coverage to internal and open-source projects. Work effectively with globally distributed engineering teams. Behavioral traits that we are looking for: Curiosity and Learning Agility: Demonstrates a passio

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, norma

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Snowflake is at the forefront of the data revolution, committed to building the world’s greatest data and applications platform. Our ‘get it done’ culture allows everyone at Snowflake to have an equal opportunity to innovate, create work with a lasting impact, and excel in a culture of collaboration. Snowflake is rapidly growing its Alliances & Channel Organization. Value Added Reseller (VAR) and Reseller partners are an integral part of Snowflake's business, helping customers unlock the power of the Snowflake AI Data Cloud. We are looking for a self-driven go-getter to help grow VAR and Reseller partners into profitable revenue streams and accelerate the growth of the business across EMEA South. We are seeking a candidate with a proven track record of successfully working with current and potential partners in the EMEA VAR and Reseller community. The ideal candidate will have strong business acumen in growing a business, channel, or partnership, with a passion for working closely with sales teams throughout the sales cycle. Knowledge of the broader data & AI platform ecosystem, products, and competition is essential to influence and be the partner’s trusted advisor. Success in this position requires the candidate to be adept at managing relationships, executing pro

We are hiring a Senior AI Engineer to build and operate production AI systems and infrastructure. In this role, you will work on AI agents, MCP servers, agent evaluation, and Temporal-based workflow frameworks. This is a hands-on engineering role for someone who can move between backend systems, AI application development, deployment, and production support. You will help build reliable services and frameworks that make AI agents usable, measurable, and dependable in production. What you’ll be doing: Build and deploy AI agents that use tools, call services, manage context, and execute multi-step tasks. Develop and maintain MCP servers and tool integrations used by internal AI systems. Build workflow infrastructure using Temporal or similar orchestration frameworks. Design and implement agent evaluation flows, test suites, quality metrics, and feedback loops. Design backend services, APIs, persistence layers, and platform components for AI applications. Work with SQL and NoSQL databases, queues, distributed systems, and cloud-native infrastructure. Build CI/CD pipelines, Docker images, Kubernetes deployments, monitoring, and operational tooling. Debug production issues across services, workflows, agents, tools, models, and infrastructure. Collaborate with engineering, product, infrastructure, and data science teams to turn requirements into reliable systems. What we need to see: 5+ years of experience and a practical understanding of how AI agents are built, including tool use, state, context, and multi-step execution. Bachelors degree or equivalent experience. Experience with at least one open-source agent or LLM application framework, such as LangChain/LangGraph. Strong software engineering background, especially in backend development. Experience with distributed systems, workflow orchestration, and prod

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Senior AI Engineer, Big Data & AI Solutions Overview The Security Solutions Data Science team develops AI and machine learning capabilities that help protect the global payments ecosystem from fraud and cyber threats. Supporting Mastercard’s Safety Net product, the team develops and enhances models that analyze billions of transactions and identify suspicious activity. As a Senior AI Engineer, you will work at the intersection of big data engineering and AI/ML, helping build and support the data, features, models, and production processes behind fraud detection. Ideal Candidate Profile You are a big data and AI/ML engineer who has used Apache Spark in a professional environment and has hands-on experience working within CI/CD-driven engineering environments. You understand how large-scale data moves from raw inputs through data and feature pipelines into machine learning models, and how those models are deployed, monitored, and improved in production. Large-scale data → Spark/PySpark → Data & Feature Pipelines → ML models → Production Deployment→ CI/CD→Monitoring & Drift Detection → Optimization & Retraining Experience with Generative AI, RAG, LLMs, or agentic AI is valuable, but the foundation for this role is large-scale data engineering and production AI/M

Block is one company built from many blocks, all united by the same purpose: economic empowerment. We build technology that increases access and independence for sellers, individuals, and artists around the world. Working at Block means working on and across our brands—Square, Cash App, Afterpay, TIDAL, Spiral, and Proto— in roles that connect, enable, and push all of them forward faster. We’re remote-first by design and operate with intelligence at the center of everything we do. At Block, you'll be challenged by some of the brightest, most creative minds in the industry and have the tools and autonomy to do the most meaningful work of your career The Role The Network Edge Security team builds the tools that provide important insights to our API Security posture and trustworthiness of traffic flowing into our systems. Our capabilities use machine learning, streaming analytics, and significant data analysis to provide insights into understanding our network edge risk exposure and the true sources of our request flows. The team’s primary focus is up-leveling our API security posture for downstream products and services. However, there is also a need at times to respond quickly to emerging threats to take action at the edge to provide immediate relief. The team is small, requiring nimbleness and flexibility to stay ahead of threat actors. You should have a strong passion for the problem space – needing to understand a significant range of knowledge around Content Distribution Networks (CDN), analytical tools, network protocols and behavior, as well as authentication and authorization systems. You will work closely with many teams across the organization both engineering and operational to gain the insights required to understand the problem space as well as monitor the effectiveness of our solutions. You Will Respond and mitigate emerging threats and attacks at our network edge by leveraging WAF rules, bot vendors, and other CDN-native technologies Help build ou

About Rubrik Rubrik is on a mission to secure the world’s data. As the Zero Trust Data Security™ Company, we deliver data resilience, data observability, and data recovery for cloud, SaaS, and enterprise environments. We help organizations keep their data secure, continuously available, and resilient against cyberattacks, malicious insiders, and operational disruptions. We are looking for a high-energy, results-driven IT Contract Recruiter to join our Talent Acquisition team. In this role, you will play a critical part in shaping the future of Rubrik by sourcing, attracting, and hiring top-tier technology talent. Role Overview As an IT Contract Recruiter, you will manage full-lifecycle recruiting for a variety of technical and IT positions across the enterprise. You will collaborate closely with IT hiring managers and cross-functional teams to identify candidates who possess exceptional technical skills and align with Rubrik’s high-performance culture. Key Responsibilities Full-Cycle Recruiting: Own end-to-end recruitment for complex IT and technology positions, Program Managers, AI Architects, & AI Engineers Sourcing & Pipeline Building: Proactively source passive candidates through LinkedIn Recruiter, job boards, industry networks, employee referrals, and specialized technical communities. Stakeholder Management: Partner directly with hiring managers to understand team structures, role requirements, and headcount priorities; deliver clear hiring strategies and regular candidate pipeline updates. Candidate Experience: Screen resumes, conduct initial phone interviews, evaluate candidate technical backgrounds, and manage candidate progression through the interview workflow to ensure a world-class candidate experience. Market Intelligence: Stay informed on IT hiring trends, talent availability, and competitive compensation packages to advise hiring managers effectively. Minimum Qualifications 5+ years of full-lifecycle technical recruitment experience (agency,

REQ ID: FEQ227R147 Location: Germany (Remote/Hybrid) - will require travel across Europe Recruiter: Dina Hussain About the Role We are looking for a Lead Solution Architect focused on Generative AI to support our EMEA Emerging Digital Natives and Startups business unit — one of the fastest-moving, most technically ambitious patches in EMEA. Our customers are digital-native and cloud-native companies who build on the frontier: they adopt GenAI early, scale fast, and expect their technical partners to be as sharp as their own engineers. You will be the go-to expert our Account Teams and customers turn to when a GenAI or LLM use case needs to move from an ambitious idea to a production-grade reality across our high-growth accounts. This is a highly technical, customer-facing individual contributor role for someone who lives at the frontier of applied GenAI. You will shape architectures, prove out the hard problems, challenge and influence customer roadmaps, and raise the GenAI capability of the entire EMEA Emerging DNB field organisation. What You'll Do Serve as the deep technical authority on Generative AI, LLMs, and applied machine learning for the EMEA Emerging DNB business unit, supporting the most strategic and complex customer engagements across our digital-native and born-in-the-cloud accounts. Partner with our EMEA Emerging DNB Solutions Architects, Solutions Engineers, and Account teams to scope, design, and de-risk GenAI use cases — from retrieval-augmented generation and agentic systems to fine-tuning and evaluation. Build hands-on proofs of concept and reference implementations that operationalise large-scale LLM and deep learning workloads on Databricks (Mosaic AI, MLflow, Model Serving, Vector Search, Unity Catalog governance), tuned to the fast iteration cycles Emerging DNB customers expect. Lead fine-tuning and model-customisation engagements on open LLMs (e.g., Llama-family models), including judge-based and label-efficient evaluation approaches for do

REQ ID: FEQ227R147 Location: France (Remote/Hybrid) - will require travel across Europe Recruiter: Dina Hussain About the Role We are looking for a Lead Solution Architect focused on Generative AI to support our EMEA Emerging Digital Natives and Startups business unit — one of the fastest-moving, most technically ambitious patches in EMEA. Our customers are digital-native and cloud-native companies who build on the frontier: they adopt GenAI early, scale fast, and expect their technical partners to be as sharp as their own engineers. You will be the go-to expert our Account Teams and customers turn to when a GenAI or LLM use case needs to move from an ambitious idea to a production-grade reality across our high-growth accounts. This is a highly technical, customer-facing individual contributor role for someone who lives at the frontier of applied GenAI. You will shape architectures, prove out the hard problems, challenge and influence customer roadmaps, and raise the GenAI capability of the entire EMEA Emerging DNB field organisation. What You'll Do Serve as the deep technical authority on Generative AI, LLMs, and applied machine learning for the EMEA Emerging DNB business unit, supporting the most strategic and complex customer engagements across our digital-native and born-in-the-cloud accounts. Partner with our EMEA Emerging DNB Solutions Architects, Solutions Engineers, and Account teams to scope, design, and de-risk GenAI use cases — from retrieval-augmented generation and agentic systems to fine-tuning and evaluation. Build hands-on proofs of concept and reference implementations that operationalise large-scale LLM and deep learning workloads on Databricks (Mosaic AI, MLflow, Model Serving, Vector Search, Unity Catalog governance), tuned to the fast iteration cycles Emerging DNB customers expect. Lead fine-tuning and model-customisation engagements on open LLMs (e.g., Llama-family models), including judge-based and label-efficient evaluation approaches for dom

REQ ID: FEQ227R147 Location: Sweden (Remote/Hybrid) - will require travel across Europe Recruiter: Dina Hussain About the Role We are looking for a Lead Solution Architect focused on Generative AI to support our EMEA Emerging Digital Natives and Startups business unit — one of the fastest-moving, most technically ambitious patches in EMEA. Our customers are digital-native and cloud-native companies who build on the frontier: they adopt GenAI early, scale fast, and expect their technical partners to be as sharp as their own engineers. You will be the go-to expert our Account Teams and customers turn to when a GenAI or LLM use case needs to move from an ambitious idea to a production-grade reality across our high-growth accounts. This is a highly technical, customer-facing individual contributor role for someone who lives at the frontier of applied GenAI. You will shape architectures, prove out the hard problems, challenge and influence customer roadmaps, and raise the GenAI capability of the entire EMEA Emerging DNB field organisation. What You'll Do Serve as the deep technical authority on Generative AI, LLMs, and applied machine learning for the EMEA Emerging DNB business unit, supporting the most strategic and complex customer engagements across our digital-native and born-in-the-cloud accounts. Partner with our EMEA Emerging DNB Solutions Architects, Solutions Engineers, and Account teams to scope, design, and de-risk GenAI use cases — from retrieval-augmented generation and agentic systems to fine-tuning and evaluation. Build hands-on proofs of concept and reference implementations that operationalise large-scale LLM and deep learning workloads on Databricks (Mosaic AI, MLflow, Model Serving, Vector Search, Unity Catalog governance), tuned to the fast iteration cycles Emerging DNB customers expect. Lead fine-tuning and model-customisation engagements on open LLMs (e.g., Llama-family models), including judge-based and label-efficient evaluation approaches for dom