דרושים » AI » Senior AI Platform Engineer - Sovereign AI Engineering

Required Senior ML Platform Engineer - Sovereign AI Engineering
The Dream Job
It starts with you - an engineer driven to build the ML platform that turns research into reliable, production-grade intelligence. You care about reproducibility, low-friction experimentation, and infrastructure that earns the trust of the scientists and researchers who depend on it daily. You'll architect and ship our ML platform - training pipelines, model serving, feature stores, experiment tracking, and compute orchestration - turning models into production capabilities across cloud and on-prem, including air-gapped deployments. A significant part of the platform supports large language models, with unique challenges across training, evaluation, and inference in mission-critical environments.
If you want to make a meaningful impact, join our mission and build the ML platform that drives Sovereign AI products - this role is for you.
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.

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.

We are seeking an experienced, hands-on Senior AI Engineer to join the Generative AI applications Platform group and lead the backend implementation and architecture of AI/LLM solutions - from agent graphs and tooling to RAG, streaming, and production deployment.
As a Senior AI Engineer you will
Design and own agent architectures - Build and evolve graph-based agent workflows (multi-node LLM flows, tool execution, routing, human-in-the-loop review gates) using LangGraph, with clear state schemas, checkpointing, and streaming to production.
Turn product and user needs into backend AI - Work with Engineers, Product, and Analysts to translate business problems into technical requirements and implementations, including agent types, tools, RAG pipelines, and configuration-driven behavior.
Design, develop, and deploy GenAI capabilities end-to-end - LangChain tools and integrations, RAG (retrievers, vector stores, agentic flows), structured outputs, and APIs for chat, Copilot-style integrations, and MCP.
Raise the bar on quality and reliability - Establish patterns for observability (e.g., LangSmith), error handling, content safety, bounded autonomy (tool schemas, review workflows), and evaluation systems so that AI behavior is predictable and auditable.
Mentor and align the team - Provide technical guidance on LLM backend architecture and LangGraph/LangChain best practices so the team can iterate quickly and safely.

Were looking for a hands-on AI Engineer
to build and own the agentic workflows at the heart of our security intelligence engine. This is a
backend-focused engineering role
: youll design, build, and maintain the autonomous systems that execute security workflows end-to-end - taking capabilities from research prototype to scalable, production-grade infrastructure.
Youll be working at the edge of applied AI, turning LLMs and agentic frameworks into reliable, observable systems that operate in real customer environments.
What Youll Build:
Agentic Workflows (Core Focus): Design and maintain the orchestration backbone for multi-step, autonomous agents that investigate, reason about, and act on complex security operations.
Internal AI Infrastructure: Contribute to a shared platform for models, data pipelines, training, evaluation, and observability that the whole AI team builds on.
Responsibilities:
Build and maintain the agentic workflow engine - orchestration, tool use, state management, retries, and evaluation - that powers our AI-driven security features.
Develop the backend services and APIs that deploy AI capabilities safely and at scale.
Collaborate with AI Researchers to translate findings into production-grade autonomous workflows.
Own reliability, observability, and performance of the agentic systems in production.

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.

Were Hiring Senior Platform Engineer
About The Role:
As a Senior Platform Engineer, you will build the shared backend platform and AI enablement layer that every product team builds on top of, and you will be a key design partner across R&D - helping teams shape architectures for new AI-powered features, backend services, and agentic systems.
This is a force-multiplier role: instead of shipping one product feature, you raise the velocity and quality of every team. You will make key architectural decisions about how AI is integrated, how services communicate, and how engineers measure and improve what they ship - and you will coach and review designs across the org so we move fast without compromising on quality.
What You'll Do
Lead and partner on architecture and design across R&D - running design reviews, shaping technical proposals, and helping teams choose the right patterns for AI, backend, and data-driven systems
Design and build core backend platform services and SDKs (auth, eventing, feature flags, configuration, data access) that product teams compose into AI-powered features
Build the AI enablement layer: shared LLM gateways, prompt and agent frameworks, evaluation and tracing tooling, model routing, guardrails, and cost/latency controls - so every team can adopt LLMs and agents safely and consistently
Define and own platform processes that improve engineering velocity and quality: service templates, paved-road patterns, code review standards, release workflows, and golden-path documentation
Build the observability and quality story for AI features end-to-end: structured logging, metrics, distributed tracing, LLM-call instrumentation, prompt/response evaluations, and regression detection
Research, prototype, and lead the selective adoption of new AI tooling, agent frameworks, and backend technologies into the platform.

We are hiring an AI Researcher to join the research team building the next generation of AI-native security systems. You will work alongside security and threat researchers to build large-scale AI agents that reason over software, code, endpoint activity, and security signals to detect malicious behavior, uncover vulnerabilities, assess risk, and make autonomous security decisions in real-world production environments. We are entering the Mythos era - where attackers operate at machine speed using autonomous systems and AI-generated software, and defenders must evolve the same way. We use state-of-the-art frontier models, including access to Mythos, to build reliable AI-native security systems at global scale. You will help design the evaluations, harnesses, and reliability infrastructure that make autonomous agents dependable under real customer load, while collaborating with leading AI organizations including Anthropic on initiatives such as Glasswing. This is an opportunity to work at the frontier of AI, autonomous systems, and cybersecurity while helping define how the next generation of security systems will operate.
Key Responsibilities
Build AI agents and autonomous security systems that reason over software, code, endpoint activity, MCPs, and security signals to detect malicious behavior, uncover vulnerabilities, and assess risk at production scale.
Develop systems, tooling, and infrastructure that enable agents to autonomously investigate threats, hunt for malware in massive datasets, and operate reliably in complex security environments.
Design and run experiments to evaluate frontier-model and agent capabilities in realistic adversarial scenarios, including benchmark creation, large-scale datasets, automated evaluations, and human-in-the-loop review systems.
Build the evaluation harnesses, observability systems, and reliability infrastructure required to make autonomous agents accurate, scalable, and dependable under real customer load.
Engineer for scale and performance across large distributed AI systems, including inference optimization, orchestration, batching, caching, cost controls, and graceful degradation under high demand.
Continuously evaluate emerging models, agent architectures, prompting techniques, and research directions to ensure our systems remain at the frontier of AI-native cybersecurity.
Rapidly prototype and test new approaches across reasoning, autonomy, evaluations, and security workflows as the AI landscape evolves.
Partner closely with threat and security researchers to extract domain expertise, translate analyst reasoning into AI workflows, and enable new forms of automation and autonomous investigation.
Collaborate with leading AI and security researchers to shape the future of AI-native cybersecurity as the industry transitions into the Mythos era.
Senior candidates will help define research direction, shape technical strategy, identify high-leverage problems, and influence how autonomous AI systems are deployed across the organization.

we are building the technical layer that brings AI-native code quality into real engineering workflows. As our partnership ecosystem grows, were looking for a principal-level engineer to turn strategic integrations into shipped products.
In this role, youll be a technical visionary and builder. You will design and implement the next generation of Model Context Protocol (MCP) servers, APIs, and platform integrations for key strategic partners like cloud providers, Anthropic, Cursor, and OpenAI. This is a high-leverage role where your expertise will directly shape how our company- the missing quality-focused system-plugs into the complex, autonomous, multi-agent development environments of tomorrow.
Ideal for someone who thinks deeply about developer experience, moves fast, and enjoys building technical infrastructure that scales through partnerships.
Mission: Make our companyunavoidable across the Agentic SDLC. Own the integration playbook that brings our company into every critical developer surface - IDEs, repos, CI/CD, code review, cloud platforms, and the AI agents shaping how software gets built. Build the technical layer that turns our company into the default quality, governance, and trust gate across the SDLC, translating cutting-edge research into integrations that scale, ship, and become embedded in how teams work.
Responsibilities
Wrap our companys capabilities as a public, composable surface. Turn our company Review, Aware, and Skills into clean, documented building blocks that any partners engineering team can adopt without hand-holding. Treat the public API, SDK, and MCP surface as a product - versioned, stable, well-documented, and obsessively focused on DX.
Be the engineering counterpart on partner conversations. Sit shoulder-to-shoulder with the Product Partnerships Lead in partner discussions. Translate ambiguous we want to integrate conversations into concrete technical scopes, integration paths (MCP vs. SDK vs. API), and shipping timelines. Be credible enough that a partners principal engineer takes the conversation seriously the first time.
Ship marketplace and cloud integrations. Own the technical execution behind cloud marketplace launches (AWS, Azure, GCP) - deployment artifacts, SaaS metering, private offer plumbing - so the commercial motion is never bottlenecked by engineering.
Define the integration playbook. Make the path from new partner interested to integration live repeatable. Document the patterns, build the reusable primitives, and reduce the per-partner engineering cost over time so the team can scale partnership volume without scaling headcount linearly.
Hold the DX bar across every partner-facing surface. Docs that work the first time. SDKs that dont surprise. Errors that explain themselves. Examples that run. If a partners engineer cant get to hello world

We're hiring a software engineer to work directly with our business teams - finance, sales, legal - building tools and automations that help them do their jobs better. You'll write production code and own what you build, but most of your time will be spent understanding business processes and how to impact them.
Most business teams have more day-to-day operational pain than they realize, and a lot more they could be doing with AI than they know. Your job is to find those gaps and build something that fixes them. Success looks like the team being able to move faster and spend their time on the work that matters, because everything else just runs.
You'll have a lot of autonomy in this role. You'll decide what to work on, build it, ship it, and see whether it actually made a difference. You'll be doing this as part of a small, senior AI team, so while the day-to-day work is independent, you'll have other engineers to collaborate with and a shared bar for how we build things.
What You'll Be Doing:
Build & Engineer:
Take the lead on technical implementation and be the owner of how it all works - picking the right tool for the job (code, no-code, or otherwise) and keeping the knowledge of the system in your handsDesign and deliver end-to-end AI solutions - from first stakeholder conversation through production deployment and iteration
Implement LLM-powered systems: RAG pipelines, AI agents, multi-step automations, and tool-integrated workflows
Integrate solutions with enterprise systems (Salesforce, NetSuite, Slack, Data platforms) and build for production: observable, reliable, and cost-optimized
Embed With the Business:
Work directly with Finance, Sales, HR, Legal, and Customer Success to identify and scope high-leverage AI opportunities
Translate ambiguous business needs into concrete engineering plans; own both the solution definition and the build
Help business stakeholders develop AI fluency and become stronger partners over time

We are seeking a Senior AI Engineer to join our core product organization, where you will design, build, and scale next-generation AI systems powering real-world cybersecurity use cases across our diverse product portfolio (Posture, Detection, and CTI). This role focuses on developing production-grade systems leveraging LLMs, advanced machine learning, and agent-based architectures.
You will join a team within our Cyber R&D organization-leading the companys core product portfolio- while driving AI innovation and establishing engineering best practices across the domain. The team focuses on building and optimizing large-scale AI systems, including LLM-based solutions and advanced multi-agent workflows, working closely with data scientists and researchers to bring ideas into production.
Responsibilities
Design, build, and own end-to-end AI solutions- from data collection and preprocessing to model training, evaluation, and production deployment.
Optimize systems for performance, scalability, and reliability in production environments.
Collaborate closely with product, design, and engineering teams to identify and deliver AI-driven capabilities that address real customer needs.
Stay up to date with emerging AI/ML technologies, frameworks, and best practices, and apply them where they create real impact.
Work across the stack, contributing to backend systems and data pipelines that support large-scale AI applications.
Troubleshoot and resolve complex system issues, including performance bottlenecks, race conditions, and memory-related challenges.
Approach problems with a strong analytical mindset, delivering robust solutions while contributing to a high-performing, collaborative team environment.