דרושים » הנדסה » Applied 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 company ronauts 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.

A security company, is building an internal AI capability across the organization: agents that perform real tasks as the organization, with ROI and security assessed at every step. We are looking for a senior engineer who owns this end to end - a hands-on builder who also carries the senior function: setting the technical strategy, making the architecture calls, and being the person leadership trusts on internal AI.
This is not a pure IC build role and not a pure strategy role. You will personally build the flagship systems - production agents, the context layer, the evaluation and safety stack - while also deciding what gets built, in what order, and to what standard. You will work across R&D, IT, Security, BizOps, Customer, Sales, Marketing, People, and Finance, with the AI Transformation Officer owning the business portfolio.
What you'll do - strategy and senior function:
Own the technical strategy for our internal AI transformation: target architecture, model and vendor choices, build-vs-buy calls.
Shape the AI portfolio: which workflows to automate, in what sequence, with what success metrics - and push back when the wrong thing is being built.
Set and enforce the bar on ROI and security for every AI system: cost, adoption, cycle-time impact, access control, and auditability are reviewed per project, not after launch.
Act as the senior technical voice for internal AI: advise leadership, review AI initiatives across the org.
Turn one-off builds into a real internal platform: reusable patterns, shared services, and standards that AI Champions and teams adopt.
Mentor AI Champions; raise the org's practical AI level.
Manage our AI- Champions Community.
Continuously evaluate models, vendors, and frameworks, and keep us agile.
What you'll do - hands-on build:
Personally design, build, deploy, and own the flagship internal agents and AI workflows, from prototype through production.
Build and evolve our AI context layer: connectors, APIs, retrieval services, metadata, identity and authorization, controls, and tool interfaces.
Create safe action tools for agents and infrastructure for agents to work.
Build RAG, tool-calling, structured-output, and multi-step agentic workflows using the right model and architecture for each problem.
Own the production quality bar for what you ship: evaluation datasets and automated checks (accuracy, groundedness, task completion, safety, cost etc.)
Work directly with business owners to map processes, test with users, and drive each pilot to a clear scale-or-stop decision.

You will be part of the Platform team, reporting to the VP of R&D. This is a hands-on IC role at the crossroads of data science, software engineering and cloud infrastructure: you design the service, you build every layer of it, you ship it, you measure what it does for the products that use it, and you maintain and extend it with the features its users need.
Responsibilities
Own services end to end: From a one-page design through API, SDK, UI and infrastructure to production, built to the platforms service standards: tested at every level, auto-deployed on merge, with live status and cost per job.
Build data services at scale: Spark and EMR jobs over the lakehouse, orchestrated in Airflow, that resolve identities, join vendor feeds and score millions of records per run, and that get faster and cheaper with every release.
Put models and LLMs into production: Foundation model, embedding, classification and agent services, each with an evaluation harness behind it, so a model change is measured before a product feels it.
Build the full stack: Backend services, web consoles and SDKs that an engineer, a product team or an agent can use self-serve, with a user guide for people and a playbook for agents.
Run what you ship: Monitoring, alerting, retries and cost per job. You are the first responder when your service is red, and you announce every release to its users with measured numbers.
Work AI-native: AI coding agents are part of how we build every day. You drive them, review their output critically, and build services that agents consume as easily as people do.

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-native engineer to build the autonomous agents that power our SalesOS. You'll work directly under the engineering head and the founders, and you'll be the one bringing the agents to life. The architecture gets set above you; the agents get built by you. Do this well and you grow fast.
We're now bringing in the engineer who will turn agent designs into shipped, reliable systems - the discovery prompts, the objection-handling logic, the orchestration that decides what the coach says and when.
This role sits close to the heart of the product. The person who gets this right will write code that real reps lean on during real deals.
A word of honesty:
This is an early-team role at a fast-moving company, which means it is hands-on and unglamorous in the way that matters. You will write the agent code, not just diagram it. You will build the eval harness yourself. You will chase down why a coaching response was wrong at 9pm because a customer demo is tomorrow. You will work from a product requirement, figure out the right agent design, and live with the calls you make. The senior team sets the architecture, but inside it you own the implementation end to end - and "it works on my machine" is not the bar. If you want a research seat where you read papers and prototype in notebooks, this is the wrong role. If you want to ship autonomous agents into production and watch them earn their keep on live sales calls, read on.
What you'll do:
Build and iterate the agents themselves - from design to production: coaching reps, analyzing deals, surfacing insight in real time.
Build tool-use and integrations that let agents reach CRMs, transcripts, calendars, and internal data (Gong, Fireflies, Stripe, Apollo, Resend).
Build prompt pipelines for structured outputs, classification, summarization, and coaching feedback - and the eval harnesses that prove they're good.
Write orchestration code that manages agent state, retries, fallbacks, and human-in-the-loop checkpoints - including the failure paths most people skip.
Own the database operations, FastAPI endpoints, and background workers that the agent workflows run on.

We're looking for an Experienced AI Product Marketing Manager someone who lives and breathes AI and wants to channel that into marketing. Think of this as the growth hacker of 2026, you're AI-native first, marketer second. AI tools are your default way of working, You build with Claude Code or OpenClaw instead of spreadsheets and slide decks. If you've ever built an AI agent for fun, automated a workflow because you were bored, or shipped a side project using vibe coding - and you're excited about applying that energy to marketing - this role is for you.
In your day-to-day, you will:
Own marketing projects end-to-end - from strategy to execution - using AI as your primary working environment, not just an assistant
Create campaigns, landing pages, product content, and customer stories at a speed and volume that would be impossible without AI fluency
Build your own tools, automations, and AI workflows to solve marketing problems
Run growth experiments across channels, rapidly testing messaging, creatives, and audiences to find what drives sign-ups and engagement
Lead product launches alongside Product and Growth teams - shaping positioning, messaging, and go-to-market plans
Stay on the bleeding edge of AI capabilities, constantly scouting new models and tools and figuring out how to apply them to marketing before anyone else does.

Morning is looking for a Senior Software Engineer with a builder mindset and a strong backend orientation — someone who enjoys taking ideas from concept to production, working across different parts of the stack, and doing whatever it takes to build great products. At Morning, engineers go beyond traditional role boundaries, taking ownership of solutions end to end rather than handing work off between specialized teams. We value people who are comfortable figuring things out, learning new technologies, and using AI tools to expand what they can build. You'll join our Delivery team, working on both existing products and new features. What you'll do
* Build and ship new features and improve existing products, taking ideas from initial problem-solving through to production.
* Work across the stack and beyond traditional engineering boundaries, taking a hands-on approach to solving problems and delivering complete solutions.
* Use AI-powered development tools extensively to explore solutions, write and improve code, debug issues, and accelerate development.
* Take ownership of the quality and reliability of what you build, critically evaluating AI-generated code and validating solutions before shipping.
* Explore new technologies and approaches to find better ways to build and deliver products. Why join Morning? Our customers rely on us to manage their financial data, so quality, reliability, and trust matter. We believe great engineers shouldn't be limited by rigid role boundaries. Our team takes a hands-on, end-to-end approach to building products — combining engineering, AI tools, experimentation, and whatever skills are needed to turn ideas into reality. You'll have room to take ownership, experiment, and make a meaningful impact, with the opportunity to see your work reach customers without long development cycles.

Advantages:

* Experience working with Databricks or Spark.
* Experience in fintech or other regulated environments.

We are looking for an AI 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:
Youll design, build, and ship the AI systems behind Alex: production agents that reason over customer identity data, call real tools, and complete IAM tasks end to end. Starting from a cybersecurity need, youll frame the technical problem, choose an approach, and own the solution through production: architecture, implementation, evaluation, deployment, and iteration on real customer behavior.
Youll build on our companys shared agent infrastructure and extend it: new tools and integrations, workflow orchestration, context and memory management, evaluation harnesses, and the guardrails that keep an autonomous agent safe inside a customers environment.
This is a hands-on engineering role, not a research position. The goal is not to prove that an idea can work; it is to make it useful, reliable, and observable for customers in production.
What You'll Do:
Design, implement, and ship AI-powered features for complex cybersecurity workflows, from prototype to production.
Build the systems that make agents work in practice: tool integrations, planning and orchestration logic, context management, structured outputs, and recovery from failures.
Choose the right technical approach for each problem, combining models, deterministic logic, data, and conventional software where appropriate.
Define quality for each use case and build the evaluation pipelines that measure correctness, task completion, consistency, safety, latency, and cost, and wire them into CI and release.
Debug agent behavior in production: trace failures to their root cause in the prompt, tool, data, or architecture, and fix it.
Own the reliability, observability, and cost of the agent systems you build.
Collaborate with cybersecurity experts, product, and platform engineers throughout development.

You'll be a senior engineer on a deliberately flat, entrepreneurial team. Day to day you'll design and build agentic AI systems - agents, workflows, the orchestration layer that ties them together, and the infrastructure that makes all of it reliable at scale. This is hands-on building, not oversight.
What you'll do
Design and build agentic workflows and the AI systems behind them - agents, the orchestration layer, and the infrastructure that runs them reliably
Take significant, ambiguous areas of the system and drive them from rough idea to working capability
Help scale the platform from a first working workflow to something used across the marketing organization
Contribute to evaluation, quality, and reliability - making sure systems don't just run, but run well
Work directly with the people who'll use what we build, and translate their needs into systems.

We are looking for a Senior AI Ops Engineer to join our AIOps team and help build the AI-powered platforms, agents, and workflows that enable engineering teams across our company.
This is a hands-on role for someone who can take AI solutions from idea to production, build the infrastructure around them, and create the observability needed to understand and improve how they behave in the real world.
Job responsibilities
Build and operate production-grade AI agents, AI-enabled tools, and workflows that empower developers and operational teams.
Design agentic systems using frameworks such as LangGraph, LangChain, and Supervisor, including RAG, MCP integrations, tool calling, and guardrails.
Build and evolve an AI SDLC harness that helps teams plan, implement, validate, govern, and safely deliver AI-assisted software changes.
Lead AI observability with Langfuse and OpenTelemetry, including tracing, evaluations, quality measurement, and cost analysis.
Build automated evaluation frameworks, including LLM-as-a-judge, to improve the reliability and quality of AI systems.
Design and maintain the infrastructure, CI/CD pipelines, GitOps workflows, and security controls that support AI services.
Develop internal developer tooling and automation that removes friction across the engineering organization.
Partner with engineering and product teams to turn real operational challenges into scalable AI-powered solutions.