דרושים » AI » Sr. GTM Specialist AI Infrastructure

Required Forward Deployed Engineer III, GenAI, Cloud
About the job
As a GenAI Forward Deployed Engineer, you will be an embedded builder bridging the gap between frontier AI products and production-grade reality for our customers. You will function as a builder-consultant, moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customers environment.
In this role, you will manage blockers to production including solving the integration complexities, data readiness issues, and state-management issues that prevent AI from reaching enterprise-grade maturity. By embedding with accounts, you will serve a dual purpose: providing white-glove deployment of AI systems and acting as a critical feedback loop, transforming real-world field insights into our future product roadmap.
It's an exciting time to join our Go-To-Market team, leading the AI revolution for businesses worldwide. Youll excel by leveraging our brand credibility-a legacy built on inventing foundational technologies and proven at scale. Well provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. Were a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era-the market is yours.
Responsibilities
Serve as the lead developer for AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, Model Context Protocol (MCP) servers) that drive measurable return on investment.
Architect and code the connective tissue between ourAI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
Identify repeatable field patterns and technical friction points in our AI stack, converting them into reusable modules or product feature requests for the Engineering teams.
Drive engineering excellence by mentoring talent, co-building with customer teams, and influencing cross-functional strategies to uplevel organizational technical capabilities.

Required Forward Deployed Engineer II, GenAI, Cloud
About the job
As a Generative Artificial Intelligence (GenAI) Forward Deployed Engineer, you will be an embedded builder bridging the gap between frontier artificial intelligence (AI) products and production-grade reality for our customers. You will function as a builder-consultant, moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customers environment.
In this role, you will manage blockers to production including solving the integration complexities, data readiness issues, and state-management issues that prevent AI from reaching enterprise-grade maturity. By embedding with accounts, you will serve a dual purpose: providing white-glove deployment of AI systems and acting as a critical feedback loop, transforming real-world field insights into our future product roadmap.It's an exciting time to join our Go-To-Market team, leading the AI revolution for businesses worldwide. Youll excel by leveraging our brand credibility-a legacy built on inventing foundational technologies and proven at scale. Well provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. Were a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era-the market is yours.
Responsibilities
Serve as the primary developer for AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, Model Context Protocol (MCP) servers) that drive measurable return on investment.
Architect and code the connective tissue between our AI products and customer's live infrastructure (e.g., APIs, legacy data silos, and security perimeters).
Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
Identify repeatable field patterns and technical friction points in our AI stack, converting them into reusable modules or product feature requests for the engineering teams.
Deliver engineering excellence by mentoring talent, co-building with customer teams, and influencing cross-functional strategies to uplevel organizational technical capabilities.

We're looking for an AI Engineer who is equal parts builder, enabler, and visionary.
This is a rare opportunity to join a small, elite team at the ground floor and have outsized impact on how AI is designed, built, and shipped across a globally recognized cybersecurity platform.
If you thrive at the intersection of cutting-edge AI research and real-world production systems and you want your fingerprints on something that matters - read on.
Why Join Us?
Greenfield opportunity - you're not joining a mature team with fixed patterns, you're helping define them.
Real impact at scale - your work will influence products used by thousands of organizations worldwide.
A team of great people - small, senior, and genuinely collaborative.
Freedom to innovate - we encourage bold ideas, fast experiments, and honest feedback.
our company's AI moment - AI is a company-wide strategic priority, and this group is at the center of it.
*we are an equal opportunity employer committed to diversity and inclusion.
Key Responsibilities
What You'll Do:
Build AI infrastructure - Design and develop the foundational tools, frameworks, and pipelines that power the group's AI capabilities, with a focus on LLMs and Generative AI.
Enable AI across the team - Act as the group's AI enablement engine: establish best practices, create internal tooling, and uplift teammates to work effectively with AI systems.
Own AI agents & agentic workflows - Design, implement, and iterate on autonomous agents and multi-step AI pipelines integrated with a variety of tools and environments.
Bring AI to production - Take models and capabilities from prototype to production-grade systems - reliable, scalable, and observable.
Shape the big picture - Contribute to the group's AI strategy, not just its execution. We want someone who asks "why" before diving into "how."
Stay ahead of the curve - Continuously research and evaluate emerging AI techniques, models, and tools - and bring what's relevant back to the team.
Collaborate and communicate - Write clearly. Think clearly. Work closely with researchers, engineers, and product stakeholders to align on goals and drive outcomes.

We are looking for a Senior AI Engineer to join a strong and dynamic AI Engineering team. We are the focal point for AI initiatives, striving to constantly bring innovation and leverage AI capabilities across all company teams and products.
Today, AI is central to how we operate, across the entire organization. It allows us to move fast and release features at a rapid pace, empowers non-technical Forterians to utilize AI tools for increased efficiency, and provides the backdrop for much of the innovation currently occurring in the company.
If this kind of working environment sounds exciting to you, if you understand that Engineering is about building the most effective and elegant solution within a given set of constraints - consider applying for this position.
Why should you join us?
This is a great opportunity to be at the cutting edge of the AI revolution, helping to shape and build the AI platform for the future. Together, well build infrastructure for autonomous and interactive agents, enact AI guardrails and evaluation frameworks to ensure performance and safety, and implement state-of-the-art
AI and Agentic patterns.
This role presents a unique opportunity to enter the AI domain. For those with some experience in AI infrastructure, it offers the chance to grow within a team that is evolving us from the AI experimentation phase to building and leveraging AI-powered products.
What you will be doing:
Design, build, and maintain reusable AI capabilities - including models, tools, APIs, and platforms that power both internal and customer-facing solutions.
Develop and maintain our internal MCP server that easily and securely exposes our vast data stores to AI agents.
Create and implement robust evaluation frameworks and AI guardrails to safeguard our value and ensure model reliability.
Establish deep expertise and sustainable AI engineering practices.
Promote AI readiness and track adoption across the company to build lasting impact.
Build and optimize RAG (Retrieval-Augmented Generation) systems.
Take full ownership of projects: from gathering requirements from non-technical internal users to development, deployment, and operation.
Act as a consultant and advocate for AI engineering, helping other teams leverage the platforms and tools you build.
Partner with teams across to accelerate AI adoption and productization efforts.

Required Al Infrastructure & Reliability Engineer
What this role is really about
Youll join a 3-person platform team within our Business Technology group -owning the internal infrastructure that our AI platform and its users depend on. This isnt a product engineering role, and it isnt ticket work or babysitting pipelines someone else built. Youre building and operating the internal foundation that the company runs on. The work covers the full stack of platform engineering: core cloud infrastructure (AWS, Kubernetes, IaC), CI/CD pipelines, AI-driven infrastructure components, and the SRE and observability practice that keeps it all honest -metrics, alerting, incident response, and reliability standards. As our AI capabilities grow, so does the complexity underneath them, and staying ahead of that is central to the role. If you treat infrastructure as a product -reusable, automated, observable, and built to last -this is your kind of role.
Job responsibilities
DevOps & AI-Driven Infrastructure - own CI/CD, deployment processes, and release reliability. Build and operate cloud infrastructure that is automated, intelligent, and continuously self-improving - not just managed.
Design and build our Terraform repository and IaC pipeline from scratch -AI-assisted generation, drift detection, and policy enforcement built in.
Build AI-driven GitHub Actions pipelines -automated code review, risk assessment, and intelligent deployment decisions.
Manage Kubernetes workloads across AWS accounts -zero downtime, fully automated, nothing left behind.
Embed AI into the operational layer -proactive drift detection, automated remediation, and intelligent scaling toward a self-healing runtime.
Reliability & SRE -improve uptime, resilience, and incident response.
Define and enforce SLOs/SLIs, error budgets, and on-call practices.
Lead incident response, postmortems, and systemic reliability improvements.
Own AI-specific reliability: model latency SLOs, token quota monitoring, rate limit handling, fallback and retry strategies, and cost-per-request alerting.
Observability & Telemetry - increase visibility, reduce noise, improve troubleshooting.
Establish and continuously evolve the observability stack: metrics, logs, distributed tracing, and alerting tuned for both application and AI workloads.
AI / LLM Operations- bringing AI systems to production and operating them at scale, with a focus on reliability, performance, and trust.
Own the AI infrastructure layer: rate limits, quota management, latency SLOs, and fallback strategies (retries, circuit breakers).
Operate LLM APIs in production with resilience and cost attribution per team/model.

MA?LYS is a fast-growing global body care brand, built on a proprietary e-commerce platform, deep consumer insights, and a relentless focus on performance. Millions of women worldwide use our products, and we are on a mission to become one of the most influential body care brands globally. We operate at extreme velocity, with high standards and an obsession with results. We believe in challenging conventional thinking, making data-driven decisions, and moving quickly from idea to execution. We are looking for a Head of AI to build and lead MA?LYS' AI transformation journey, serving as a rare opportunity for a highly strategic and deeply hands-on builder who thrives at the intersection of business, technology, and execution. In this role, you will have the ultimate playground to shape how our entire company operates, influence every major function, and create a lasting competitive advantage for a leading body care brand. Initially operating as an individual contributor partnering closely with the CEO and the Management Team. Over time, you will transition to build and lead a small, high-performing team as AI becomes an increasingly critical capability across our global e-commerce and retail operations. To thrive in our high-pace environment, you must think like an owner, not a consultant, and be equally comfortable discussing macro business strategy with executives as well as writing code to solve a messy problem yourself. You are someone who embraces ambiguity, enjoys building from scratch, and is completely obsessed with execution. You possess the high emotional intelligence and empathy required to collaborate with non-technical stakeholders and build trust, yet you maintain the thick skin, resilience, and great sense of humor needed to handle direct communication and rapid structural pivots. Most importantly, you maintain an uncompromising commitment to quality and process improvement, ensuring that every automation or workflow you deploy actively drives our core business metrics forward without requiring hand-holding or constant reminders. Your life at MA?LYS will look like… • Define and lead the company's AI strategy and execution roadmap. • Identify the highest-impact opportunities for AI across all business functions. • Prioritize initiatives based on measurable business value, speed of implementation, and scalability. • Design, build, deploy, and continuously improve AI-powered workflows, tools, automations, and agents. • Translate business challenges into practical AI solutions that improve performance, efficiency, and decision-making. • Develop production-ready applications and workflows using modern AI technologies, APIs, agents, and automation frameworks. • Drive adoption and behavioral change across teams. • Partner directly with business leaders to identify opportunities and implement solutions. • Evaluate emerging technologies, models, vendors, and platforms. * Establish governance, standards, architecture, and best practices for AI development. • Define and track success metrics for every AI initiative, ensuring measurable impact on growth, productivity, speed, efficiency, or cost.
You will thrive in this role if you have… • Strong technical foundation in Computer Science, Software Engineering, Data Science, or a related field. • 5+ years of experience building technology, data, AI, automation, or software solutions in high-performance environments. • Proven experience identifying business opportunities and translating them into deployed AI products, agents, automations, or workflows that drive measurable business impact. • Hands-on experience working with modern generative AI technologies, LLMs, agent frameworks, APIs, embeddings, automation platforms, and production deployments. • Strong proficiency in Python, SQL, APIs, and data-driven application development. • Deep understanding of business operations, growth metrics, performance marketing, and operationa

we are looking for a Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
"our company's data management vision is the future of the market."- Forbes
we are the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, our company takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.
Our success has been built through intense innovation, a customer-first mentality and a team of fearless workers who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our companys growth and at a pivotal point in computing history.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

At our company, we redefine cyber defense vision by combining AI and human expertise to create products that protect nations and critical infrastructure. This is more than a job; its a Dream job. we are where we tackle real-world challenges, redefine AI and security, and make the digital world safer. Lets build something extraordinary together.
our company's AI cybersecurity platform applies a new, out-of-the-ordinary, multi-layered approach, covering endless and evolving security challenges across the entire infrastructure of the most critical and sensitive networks. Built as part of a broader sovereign AI platform, our technology is designed to operate in on-premise, private cloud, and air-gapped environments, enabling nations to maintain full control over their data, infrastructure, and AI capabilities. Central to our company's proprietary Cyber Language Models are innovative technologies that provide contextual intelligence for the future of cybersecurity.
At our company, our talented team, driven by passion, expertise, and innovative minds, inspires us daily. We are not just dreamers, we are dream-makers.
Responsibilities
Architect and evolve the AI platform - agent orchestration, LLM gateways, context engineering pipelines, evaluation infrastructure, tool-calling systems, and retrieval pipelines - through RFCs, prototypes, and design reviews.
Lead and grow a small team of AI Engineers building the agent framework, production backend services, and AI platform infrastructure - hire, mentor, pair on hard problems, and raise the bar through hands-on code and design reviews.
Contribute to critical systems, debug production incidents, and maintain enough codebase context to make sound technical calls.
Own reliability across AI and agent services - set and enforce SLAs, build observability for non-deterministic systems, and harden tool execution environments for cost and security.
Set the standard for AI engineering practices - agent testing strategies, evaluation frameworks with human-in-the-loop oversight, retrieval quality benchmarks, and CI/CD for AI systems.
Work closely with ML Platform, Data Platform, DevOps, Data Science, and Product teams across the Applied AI Engineering group - ensure the AI platform evolves to serve teams building agentic workflows across the organization.
Measure and improve developer experience - deploy friction, onboarding time, CI turnaround - as seriously as system performance.

At our company, we redefine cyber defense vision by combining AI and human expertise to create products that protect nations and critical infrastructure. This is more than a job; its a Dream job. we are where we tackle real-world challenges, redefine AI and security, and make the digital world safer. Lets build something extraordinary together.
our company's AI cybersecurity platform applies a new, out-of-the-ordinary, multi-layered approach, covering endless and evolving security challenges across the entire infrastructure of the most critical and sensitive networks. Built as part of a broader sovereign AI platform, our technology is designed to operate in on-premise, private cloud, and air-gapped environments, enabling nations to maintain full control over their data, infrastructure, and AI capabilities. Central to our company's proprietary Cyber Language Models are innovative technologies that provide contextual intelligence for the future of cybersecurity.
At our company, our talented team, driven by passion, expertise, and innovative minds, inspires us daily. We are not just dreamers, we are dream-makers.
Responsibilities
Build and operate ML training infrastructure - distributed training pipelines, compute scheduling, and reproducible experiment workflows that data scientists rely on daily.
Own model serving and inference systems - packaging, deployment, autoscaling, A/B testing, canary rollouts, and latency/cost optimization for production models.
Run feature stores, model registries, and dataset versioning - enabling self-serve feature engineering, model lineage, and reproducible experiments across teams.
Build experiment tracking and evaluation infrastructure - automated evals, comparison dashboards, drift detection, and monitoring that give teams visibility into model behavior and performance.
Build and maintain production pipelines for training, fine-tuning workflows, and serving domain models - owning reliability, reproducibility, and scale.
Build and maintain the monitoring and observability layer - model performance tracking, data and prediction drift detection, data quality validation, and alerting.
Improve performance and cost across the ML stack - training throughput, inference latency, batch vs. real-time tradeoffs, and compute cost management.
Ship shared tooling - libraries, templates, CI/CD for models, IaC, and runbooks - while collaborating across Data Platform, AI, Data Science, Engineering, and DevOps. Own architecture, documentation, and operations end-to-end.