דרושים » AI » AI Engineer

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.

Required Senior AI Platform Engineer - Sovereign AI Engineering
The Dream Job
It starts with you - an engineer driven to build the agentic AI platform that turns LLMs into reliable, production-grade capabilities. You care about clean APIs, well-defined service boundaries, and systems that teams can build on with confidence. We are AI-first across the board - every team builds and operates agents. You'll architect and ship the platform that makes this possible: agent orchestration frameworks, LLM gateways, evaluation pipelines, tool-calling infrastructure, and retrieval systems. Without this platform, agents don't ship - you own the layer that turns AI research into Sovereign AI products, deployed across cloud and on-prem environments.
If you want to make a meaningful impact, join our mission and build the agentic AI platform that drives Sovereign AI products - this role is for you.
Responsibilities
Design and build agentic systems - single and multi-agent workflows with planning, memory, context engineering, and tool use - for both internal automation and product-facing autonomous capabilities operating over long time horizons.
Build and operate the AI platform layer - LLM gateways, prompt management, structured output handling, tool-calling infrastructure, and cost/latency optimization - deployed on Kubernetes, consumed by every team for their agentic work.
Own the agent framework layer - orchestration primitives, execution environments, state management, and sandboxed tool execution - giving every team the building blocks to create and operate their own agents.
Build evaluation infrastructure that gives teams confidence in agent behavior - automated LLM and agent evals for quality, correctness, safety, latency, cost, and regressions, including human-in-the-loop oversight for mission-critical workflows.
Productionize and harden backend services (APIs, gRPC, async workers) that integrate LLMs - with proper error handling, retries, circuit breakers, and high-availability patterns.
Own RAG pipelines and retrieval systems - indexing, chunking, embedding, vector database management, filtering, and relevance tuning for production retrieval.
Optimize performance and cost across the AI stack - model routing, caching, batching, and inference cost management.
Ship shared tooling - libraries, SDKs, agent templates, and documentation - while working closely with ML Platform, Data Platform, DevOps, and other teams across the Applied AI Engineering group. Own architecture, documentation, and operations end-to-end.

We are looking for a Senior AI Engineer to design and build the intelligence layer .

You will own the agentic workflows, LLM integrations, and reasoning pipelines that allow our AI agents to autonomously analyze markets, make decisions, and drive eCom growth at scale.

What you'll do
Design & Build Agentic Workflows: Architect multi-agent pipelines - including planning, memory, tool use, and decision loops - that power autonomous media buying and growth operations.
Own LLM Integration: Select, prompt-engineer, fine-tune, and evaluate LLMs to produce reliable, high-quality outputs across diverse business tasks.
Build RAG Systems: Develop retrieval-augmented generation pipelines with vector search and context management to ground agent reasoning in real business data.
Drive Evaluation & Reliability: Define evals, build testing frameworks, and continuously improve agent output quality, consistency, and safety in production.
Collaborate Across the Stack: Work closely with backend engineers to integrate AI capabilities into core product APIs, ensuring low-latency, production-grade deployment.

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.

As an AI Engineer at our company, you will own the intelligent decision-making pipelines that turn complex workspace telemetry into autonomous security actions. You will design, build, and deploy the autonomous reasoning workflows and advanced data classification systems that drive our company's preventive operating model. Your focus will be on creating resilient, production-grade AI systems capable of deep policy comprehension, real-time prevention at the point of adoption, and autonomous remediation of existing risks.
This is a ground-floor opportunity to shape the AI strategy of a fast-growing cybersecurity company alongside a lean, elite team of builders.
WHAT YOULL DO
End-to-End Ownership: Own our AI capabilities entirely from initial research, architectural design, and prototyping, through to production deployment, optimization, and continuous monitoring.
Design & Build Agentic Workflows: Architect multi-step AI agents capable of autonomously investigating workspace risks, interpreting complex enterprise policies, and taking precise remediation actions.
Integrate Multi-Faceted ML: Bring innovation and creative thinking to our core engine. Implement diverse ML models across our entire research and product pipeline-utilizing clustering, text extraction, document analysis, and tabular data classification.
Ship Production-Grade AI: Build high-throughput, resilient, and fault-tolerant production code. You will ensure our AI pipelines and agentic workflows are highly predictable, deeply observable, and built to scale under enterprise-grade loads.
Implement Guardrails & Evaluation: Build continuous evaluation frameworks to benchmark agent accuracy, mitigate hallucinations, and enforce strict data security/privacy guardrails.

We're looking for a Senior Applied AI Scientist to sit at the frontier of AI security - turning emerging threats into the detection models that protect how AI is used inside the world's largest organizations. You'll be part of the AI Security Research department, working hand-in-hand with security researchers to translate threat intelligence into trainable signals that catch malicious behavior and security risks across the AI-powered workflows of Fortune 500 companies.
You'll bring deep technical versatility - reaching for classical ML, deep learning, or agentic based approaches based on what the problem demands, and the evaluation rigor to know when a model is truly ready for the real world. If you want to define what AI security engineering looks like, not just practice it, this role is for you.
What Youll Do:
Build, train, and ship detection models end-to-end, from raw data to production
Choose the right method for each problem - traditional ML, deep learning, fine-tuned LLMs, agents or heuristics - based on theoretical insights turned into practical results.
Partner with security researchers to turn security research outputs and domain expertise into detection capabilities
Own evaluation: design benchmarks, build labeled datasets, and define production standards
Monitor models in production across all paradigms - ML, deep learning, LLM-based, and agentic systems to track degradation and ensure reliability
Iterate fast, with a tight feedback loop between model performance and product outcomes

We are seeking talented, motivated AI builders to join our Strategic Initiatives team as part of a new AI Center of Excellence. Reporting to the Sr. Director of Engineering, this team designs and ships AI-native solutions - agents, assistants, and reusable skills - that advance our security mission and multiply the impact of teams across the company.
This is a builder role. We're looking for experts engineers fluent in modern AI application development - agent frameworks, harnesses, tool use, prompting, retrieval, and evaluation - not machine learning engineers. You will be expected to train foundation models / frontier models . You will compose capable, production-grade AI systems on top of frontier models, get them into people's hands, and bring the rigor to prove they actually work.
What you'll do:
Design, build, and bring to production ready: models, AI agents, assistants, and reusable skills that solve real problems for us and our customers.
Turn ideas into working MVPs fast, then partner with engineering to harden them into reliable, production-ready systems.
Build evaluation into everything you ship - define what "good" means for each solution, measure it, and hold the bar. Due diligence through rigorous evals is non-negotiable, not an afterthought.
Compose agentic workflows that are secure by construction, using secure harnesses along with appropriate isolation, guardrails, and controls.
Select and integrate the right frameworks, tools, and models for each job, and turn what works into reusable patterns and best practices for the COE.
Also be a part of the COE Consult team to help across the organization.
Document what you build and how it should be used, operated, and evaluated.

we are looking for a Senior II Software Engineer to join the CX Platform team, the foundational engineering team powering entire Customer Experience group. This is a high-impact, hands-on role at the intersection of backend engineering, AI infrastructure, and customer-facing product.

You'll work across the full platform stack (backend services, data pipelines, security, cost, and scale) with a meaningful and growing focus on AI infrastructure. We own the agentic platform for the entire Product Offering group: from building the LLM infrastructure and agentic workflows to ensuring they're reliable, observable, and safe in production.
What you'll be doing:

Own AI infrastructure for the Product Offering group. Design, build, and evolve the shared AI platform (agentic workflows, LLM integrations, observability, and guardrails) that CX product teams build on.
Ship agentic features end to end. Lead development of AI-driven capabilities using LangChain, LangFuse, and AWS Bedrock, from architecture through production deployment and monitoring.
Drive platform architecture. Set the technical direction for the CX backend (services, data pipelines, API patterns) with an eye for scalability, reliability, and developer experience.
Own core data foundations. Design resilient data-access patterns across Snowflake, Elasticsearch, Kafka, Redis, and MySQL; keep pipelines fast, fresh, and reliable.
Mentor and elevate. Help engineers across the CX group grow in backend craft, AI engineering, and system design thinking.
Collaborate cross-functionally. Work with product, design, and customer-facing teams to turn ambiguous problems into well-scoped, high-quality solutions.

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

We're looking for a Senior Backend Engineer to help build and scale the Machine Learning Platform that powers how uses AI across the business. You'll be part of the ML Platform team, designing the infrastructure that lets our data scientists move faster, ship smarter, and operate with confidence in production.
We believe three things matter for every role : drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work. Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role you'll:
Design and build the foundational ML platform and AI agents to accelerate data science model delivery across all business units
Architect cloud-native microservices running on Kubernetes, using infrastructure-as-code to automate model deployment and management
Own the end-to-end ML lifecycle, covering training, testing, deployment, and real-time monitoring
Evaluate and choose the right tools and technologies based on workload demands and performance requirements
Collaborate with engineering, data science, and product teams to keep ML projects aligned with business goals
Identify and fix reliability, scalability, and performance gaps before they become problems