דרושים » AI » Senior AI Systems Engineer, Agents

What you'll be doing
Agent architecture: Design the evolution from today's production single-agent system to a multi-agent one: orchestration, task decomposition, runtime and framework choices, and a migration path that does not break what design partners already rely on.
Agent capability: Own the prompts, context, skills, and tool design that make the agent genuinely good at detection engineering across multiple security platforms, not just plausible-sounding.
Evaluation platform: Build the harnesses, judges, and golden datasets that turn "the agent feels better" into a number, plus the CI gates that keep regressions from shipping.
Reliability and safety: Keep long-running agentic sessions healthy in production, and build the isolation and guardrails required of an agent working inside enterprise security environments.
Production debugging: Work real failures from production traces, and turn each one into an eval case that can never regress silently.
Technical direction: Make the calls on architecture, sequencing, and quality bar and be accountable for the outcome, including raising how AI-natively the whole team builds.
Cross-team partnership: Partner with product and customer-facing teams on what the agent should do, and with platform teams on the data and integrations it depends on.

We're looking for an AI Tech Lead to own that standard across three surfaces:
The platform - Today each agent flow is close to a bespoke implementation. You'll turn our hard-won patterns into shared components, conventions, and infrastructure so the next agent is a week of work rather than a quarter - with evaluation, observability, and cost control built in rather than bolted on.
Enablement - Miggo's advantage compounds only if the whole company is AI-fluent, not just R&D. You'll raise that fluency everywhere - engineering, research, product, GTM - through tooling, patterns, and teaching.
The voice - You'll publish the methodology: how we benchmark agentic security output, how we model residual risk, what we learned failing. This is a category-defining position and we want it argued in public.
This is a hands-on lead role with no direct reports. Your authority comes from the quality of what you build and how clearly you explain i

our company's platform runs inside the security stacks of enterprise customers, processing live detection and telemetry data from SIEM, EDR, and SOAR systems at real scale. Until now, infrastructure has been built and carried by the founding engineering team alongside everything else. You will be the first dedicated owner of it. That means designing our company's cloud foundation across GCP and AWS, building the deployment, scaling, and observability layers from the ground up, and setting the standards the rest of engineering builds on. It also means holding a bar that most startups can defer and our company cannot: we sell to security teams who audit their vendors seriously, so how we run our own infrastructure is part of how we win. This is a hands-on senior role with real architectural authority, and as engineering grows you become the person others learn cloud and infrastructure from.
What you'll be doing
Multi-cloud architecture: Own the design and implementation of our company's multi-cloud architecture across GCP and AWS, including the cross-cloud standards, boundaries, and tradeoffs that keep two providers from becoming twice the complexity.
Infrastructure as code: Build and maintain the entire environment in Terraform, so infrastructure is reviewable, reproducible, and scales without a person in the loop.
Deployment pipelines from scratch: Create the CI/CD foundation that lets a small team ship quickly and safely, with the testing, gating, and rollback paths that make fast releases boring rather than risky.
Kubernetes at data scale: Run and scale the Kubernetes and containerized workloads behind our company's security data processing, where volume, latency, and cost pressure all show up at once.
Security of our own stack: Own identity, access, secrets, network boundaries, and infrastructure hardening to a standard that holds up under customer security review.
Reliability and visibility: Build the logging, monitoring, and alerting layer that tells us something is wrong before a customer does, and make on-call sustainable as the system grows.
Technical direction: Partner directly with the founding engineering team on architectural decisions, and raise the infrastructure and cloud fluency of every engineer who joins after you.

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.
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 looking for an AI Research Engineer to build the backbone of our autonomous security platform.
our company builds AI Digital Employees who help cyber teams close the execution gap. Our first AI Digital Employee, Alex, learns, understands and takes away the burden of Identity and Access Management (IAM) tasks - proactively completing the organization's cyber objectives.
About the Role:
You'll own technically difficult problems before the solution is known. Starting with a cybersecurity need, you'll study the domain, frame the technical challenge, investigate possible approaches, and build the solution through production. You'll have access to our company's shared agent infrastructure, but the role goes far beyond configuring existing components. Many use cases will require new reasoning methods, agent architectures, evaluation techniques, data strategies, or model behavior. This is a role for someone who enjoys applied research but is motivated by outcomes. The goal is not simply to prove that an idea can work, it is to make it useful and reliable for customers.
What You'll Do:
Design and implement AI solutions for complex cybersecurity workflows.
Develop new approaches for agent reasoning, planning, tool use, context management, decision-making, and recovery from failures.
Determine the right technical approach for each problem, combining models, algorithms, data, and conventional software where appropriate.
Define quality for each use case and create evaluations that measure correctness, task completion, consistency, safety, latency, and cost.
Analyze agent behavior deeply, identify the underlying causes of failures, and improve the relevant model, data, tool, or architecture.
Collaborate with cybersecurity experts, product teams, software engineers, and AI infrastructure engineers throughout development.

Platform and infrastructure (K8s + IaC)
Architect and operate our Kubernetes platform on AWS - scaling, networking, cost, and reliability
Own infrastructure as code (Terraform) so environments are reproducible, auditable, and fast to evolve
Build the foundations that let the team provision and scale services without friction


AI agent platform - managing and scaling agents at volume
Operate and scale the orchestration layer (Temporal) that runs dozens of agents in parallel
Tune the platform for the unusual load profile of agent workloads - bursty, long-running, data-heavy, latency-sensitive
Give engineers the primitives to deploy, version, observe, and roll back agents safely under enterprise SLAs


CI/CD and developer experience
Own build and deploy pipelines end-to-end - fast, safe, boring releases
Invest in DX as a first-class product: the team treats developer experience as leverage, and you set the bar
Reduce the time from merged to in production and from idea to running experiment


Observability and reliability
Build monitoring, alerting, and tracing that make production legible - for services and for agents
Own incident response and the reliability practices that keep enterprise customers SLAs intact
Turn incidents into systemic fixes, not repeated firefighting


Security and compliance
Own secrets management, hardening, and the day-to-day security posture of the platform
Support our compliance commitments (we are Mastercard-certified, operating in fintech - the bar is high)
Build security into the pipeline so it is the default, not a gate

We are at the forefront of the AI revolution, delivering brand new accelerated compute platforms for global impact. Our Network Architecture group is seeking a talented and motivated Sr. Software Engineer to build the agentic workflows that our architects use in their daily work. The software at the center of this role is our hardware network simulation environment - you will design multi-step agent workflows over it, engineer the context that grounds them in our own specifications and source code, and optimize their runtime performance. If you are passionate about building the practical infrastructure that brings intelligent agents to life, we want to hear from you.


What you'll be doing:
Build agentic workflows - loops, graphs, and multi-step pipelines - that carry real hardware network simulation and analysis work end to end.
Engineer the context these workflows run on, turning our simulation models, specifications, design documents, and source code into context that makes agents accurate in our domain.
Work closely with network architects to understand their workflows and translate them into agent workflows they use daily.
Optimize the runtime performance of our simulation tooling on these platforms, including execution time, compute cost, and end-to-end latency.
Define evaluation and regression testing for agent workflows, so that changes to a prompt, a graph, or a context source are measurable.
Build observability across agent runs: what the agent did, where it failed, and why.
Champion guidelines for secure and reliable agent workflows, including data handling, access control, and interaction boundaries.
Serve as a key technical resource for solving sophisticated integration issues between agents and internal tooling.

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 forming a new AI Group and looking for a Senior AI Engineer to help shape it from an early stage - a greenfield, long-term effort to evolve how decisions are made across the platform using AI-driven systems. You won't just integrate APIs or build demos; you'll build the AI brain that works alongside (and increasingly drives) our core automation engine, with real production impact from day one and room to grow into technical leadership as the group scales.

Agentic AI Architecture: Design and build autonomous AI agents that analyze infrastructure in real time and make intelligent decisions. Work with modern agentic frameworks (LangGraph, PydanticAI) and conversational AI to create multi-agent systems - including troubleshooting, optimization, FinOps, and how-to agents. Leverage core LLM capabilities (tool-use, memory, retrieval) to operate safely in production.
Platform Integration & Intelligent Decision Systems: Develop MCPs to expose capabilities to AI agents that reason over infrastructure environments, metrics, configurations, and cost signals. Build integrations with tools like Slack, Jira, and AI-powered IDEs (Cursor, Windsurf) to deliver context-aware insights, from "why is this pod not scheduling?" to "how can we reduce costs by 30% safely?"
AI Model Development & MLOps: Build and deploy machine learning models that learn from infrastructure patterns - detecting the right resource policies for workloads, predicting optimal scaling triggers, and recommending GPU configurations. Own the complete ML pipeline from training to production, ensuring models are reliable, monitored, and continuously improving.
R&D AI Tools Development & Adoption: Build and embed internal AI tools to accelerate engineering, development, research, and support.
AI Tools for Business Impact: Develop AI-powered tools that help Sales and Support teams demonstrate value instantly - agents that analyze customer infrastructure, generate cost optimization reports automatically, and turn technical data into clear business recommendations.
End-to-End Ownership: Own AI systems from concept to production, ensuring they're fast (sub-2-second responses), reliable, safe, and cost-effective. Build evaluation frameworks to measure quality, implement security controls, and balance performance tradeoffs in production.
Technical Leadership: Define AI architecture and best practices as a founding member of the AI team. Make key technical decisions - choosing frameworks, designing multi-agent systems, establishing data governance - and shape how evolves from AI-enhanced internal tools to customer-facing AI products.