דרושים » תוכנה » AI Engineering Tech Lead - Hands-on

This role reports to the AI Team Lead and is based in Tel Aviv (hybrid model).

You'll own our AI reference architecture end to end - the patterns, standards, and platform capabilities that every AI initiative across engineering builds on. You'll lead architecture reviews, set the technology roadmap, and make the call on what we adopt, pilot, or retire.

Be part of our AI architecture design and ensure every AI initiative aligns with it.
Define reusable patterns for AI services, agents, retrieval systems, evaluation pipelines, and infrastructure.
Evaluate emerging technologies, vendors, and foundation models, and maintain a forward-looking roadmap.
Partner with Product leadership during early discovery on AI-intensive initiatives.

our company's product is built around an AI agent that security analysts and detection engineers work with directly. It investigates coverage questions against live enterprise security data, authors and tests detection logic, and tunes noisy alerting.
That agent is already in production with enterprise design partners. Now we need to make it dependable and scalable enough for GA.
You will own that evolution: the agent architecture, its evaluation and quality system, and the production engineering around it. This is a hands-on senior IC role with real architectural authority - you set the technical direction and you write the code.
The agent operates inside customer security environments, where a wrong action can become a customer incident. Correctness, isolation, observability, and evaluation are not polish. They are the product.
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 are looking for a Senior AI Engineer - Exploration & Prototyping.
That is this role. You take an open question, run a focused spike, and come back with numbers and a recommendation. You read the source of the frameworks you evaluate rather than trusting their marketing. You build prototypes to settle arguments.
You do not own a subsystem and you do not ship to customers, which is exactly what protects the work: exploration inside a delivery team always loses to the sprint. You sit alongside the platform, research, and forward-deployed teams, you borrow their context freely, and your output is evidence they can act on.
You will be trusted with real influence early. The recommendations you write become the architecture other people build against, so the bar is not a working demo but a defensible conclusion, including the ones that say no.
What Youll Do:
Run technical spikes that close open decisions, covering agent orchestration frameworks, real-time transport, memory protocols, agent interoperability standards, LLM selection and routing, evaluation harnesses, and the production library and stack choices underneath all of it.
Build prototypes to de-risk, standing up something real quickly, proving or disproving the thing in question, and moving on without becoming attached to the code.
Read and evaluate unfamiliar codebases, going into the source of a candidate framework to find out whether it can actually support what we need rather than what its documentation implies.
Design the measurements that make a decision defensible, building the harness, running the comparison, and reporting latency, cost, and failure behavior honestly.
Own build-versus-adopt recommendations for platform infrastructure, frameworks, and libraries, including a clear statement of what it would cost to be wrong.
Write the recommendation down. Every spike ends in a short, decisive document another engineer can act on, with the evidence, the rejected options, and the reasoning behind the call
Hand off cleanly, transferring what you learned to the team that will own the capability in production, and staying available while they pick it up.
Track the landscape across agentic infrastructure, real-time frameworks, and adjacent AI tooling, and bring forward the things that genuinely change what we can build.

We are hiring someone to lead product level and internal team AI end-to-end: from protocol and architecture decisions to server-side implementation to the AI logic that decides when and how our tools are used, to internal processes that improve R&D and product development efficiency. This is a hands-on position with managerial aspects, not an advisory role. You will be building hands on in parallel to building the process and managing/guiding the team.

What You Will be Doing
Own the strategy and roadmap for C2As AI strategy within R&D and AI agents specifically,
Lead the AI adoption effort technically within the product and across the organization as the company learns how to become more efficient with AI usage for R&D.
Design and build MCP servers that expose our APIs as well-described, reliably usable tools for AI clients.
Build the AI decision layer - the prompting, tool schemas, and agentic logic that let a model select the right API, pass the right parameters, and recover gracefully from errors.
Implement complete, production-grade features end to end: backend services, data models, APIs, and the AI integration on top, not prototypes.
Define how AI-accessible APIs are scoped, authenticated, and permissioned, so an agent can only ever do what it is authorized to do. Security is the product, this matters.
Build evaluation and observability for the AI layer: measure tool-selection accuracy, catch regressions, and trace agent behavior in production.
Set engineering standards and mentor others as the effort grows into a team.
Implement new features required by our cybersecurity product.

This role owns the AI-native GTM engine: the systems that find, enrich, reach, and route prospects and customers, and the workflows that create new opportunities for the company. You build revenue systems with AI tooling, then run them against pipeline targets. The scope is wide on purpose. You own the motion and the measurement, so theres no gap between what you build and what you can prove.

What youll own
Outbound engine. Multi-channel outbound end-to-end: list building, segmentation, messaging, sequencing, launch, and A/B iteration across email and LinkedIn. AI-driven personalization at scale, moving prospects from first touch to booked meeting with minimal manual input from sales.
Inbound conversion. Everything between the first visit and the booked meeting: on-site conversion paths, forms, chat, scoring, routing, and speed-to-lead. Instrument every step, find where intent leaks, and close the gap with automation instead of headcount. No inbound lead should sit waiting for a human to qualify it.
CRM truth and attribution. The measurement layer under everything else. If outbound, inbound, AEO, and content cant be attributed, none of it can be optimized or defended. Own data quality across HubSpot and Salesforce.
Data and enrichment. Prospect identification and signal tracking: enrichment waterfalls, buying signals, social listening, and clean push architecture into the CRM.
AI agents and automation. Claude is a teammate here, not a chatbot. Build the agents, skills, and workflows that encode our GTM playbooks and kill anything manual.
AI content and discovery. Build the writing agents that produce inbound at volume: programmatic pages, competitor comparisons, persona and vertical landing pages, localized variants, and one asset turned into fifteen across channels. AEO is the other half of this. Buyers now start in ChatGPT, Claude, Perplexity, and AI Overviews, not on a results page, so the content has to be built to be retrieved and cited: structured content and schema, presence on the review sites and communities models pull from, and a technical layer that makes our legible to crawlers.
Customer expansion. The signal engine that finds revenue inside the base: segmentation, expansion triggers, and routing each account to the right owner or the right sequence at the right time.
The stack itself. The GTM tech stack end-to-end: evaluate, buy, integrate, and kill tools as the motion evolves, and build tools with direct impact on revenue when nothing off the shelf does the job.

we're building the financial infrastructure that powers global innovation. With our cutting-edge suite of Embedded payments, cards, and lending solutions, we enable millions of businesses and consumers to transact seamlessly and securely. With 900+ employees worldwide and an R&D center of over 160 employees in Jerusalem - were reshaping how financial technology is developed and delivered.
The Role:
Staff software engineer is our most senior individual-contributor role. You report to a VP of Engineering and act as the technical force multiplier across the several teams that VP owns through their Engineering Managers. Engineering Managers own people and delivery. You own the technical bar.
Who You Are:
You turn strategy into architecture, make the hard design and build-versus-buy calls, and keep decisions coherent across every team under the VP. You work in three modes: lead technical direction, help unblock and grow teams, and build the highest-risk pieces yourself. Hands-on. Not a manager. Your leverage is architecture, code, and the engineers you make better.
What Youll Actually Be Doing:
* Partner with the VP to translate business goals into cross-team technical strategy and clear architecture.
* Own the hard technical decisions: system design, service boundaries, data models, stack choices, and the tradeoffs behind them.
* Define the standards teams build against: coding, testing, security, observability, operational readiness.
* Drive alignment across Engineering Managers and their teams so independent decisions hold up at the platform level.
* Unblock teams on the hard problems: scale, latency, reliability, thorny integrations, production incidents.
* Take initiative without being asked: spot gaps, propose the fix, and drive it across teams before it becomes a problem.
* Actively grow and mentor engineers. Invest in their technical development, coach through hard problems, and build the next tier of senior talent.
* Prototype and ship the highest-leverage, highest-risk code yourself so teams inherit a proven path.
* Set the bar for quality, security, and compliance in a regulated, Real-Time financial environment.
Why Youll Love Working Here:
* Flexible hybrid model: 3 days a week in the office - A must
* 1,000 net monthly wellness benefit - from therapy to Pilates to your kids art class
* Full Keren Hishtalmut, private health & dental insurance
* Donation matching, volunteering days, team outings, and mentorship programs
* A mission-driven culture that values ownership, trust, and meaningful impact

We need a Director to set the strategy for everything underneath it - cloud, security, and the delivery lifecycle - and to bring AI into how that infrastructure is run.

This is a director-level leadership role, not an IC stretch assignment. You will lead our DevOps organization - which already has a team leader running day-to-day delivery - and operate one level up: setting direction, owning the roadmap, making architecture and build-vs-buy calls with authority, and representing infrastructure at the leadership table. Close enough to the work to guide it in depth, senior enough to own it end to end.

What Youll be Doing:
Own cloud infrastructure and security as one strategy: architecture, scale, and the path to our next order of magnitude of growth, with our security and compliance posture (IAM, secrets, hardened baselines, SOC 2 / ISO 27001 audit readiness) built in from the start, not bolted on.
Own the SDLC end to end, and drive AI into every stage of it: measure delivery performance (lead time, deployment frequency, change failure rate) and use AI to improve each stage of the lifecycle - planning, code review, testing, release, and operations - alongside AIOps practices like automated/self-healing recovery and intelligent alerting.
Own FinOps as a strategic discipline: the cloud bill is a P&L line you're accountable for, with unit economics driven down every quarter.
Own vendor and contractual relationships across cloud, tooling, and infrastructure services - negotiating leverage, not just approving invoices.
Lead the DevOps organization through its team leader: set technical direction and standards, develop the team leader into a stronger manager, and grow the bench beneath them.
Partner as a peer with Backend, Infra, AI, Data, Product, Security and Finance leadership - infrastructure decisions get made with you in the room, not after.

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.

DealHub.AI is building the future of agentic revenue technology. Trusted by leading global businesses, we’re redefining what revenue teams can achieve with AI and agentic execution across the deal lifecycle. Our mission is simple: help businesses Own Your Revenue with greater speed, intelligence, and governance. As an Solution Expert you will serve as the technical trusted advisor for our customers, bridging AI capabilities with their specific business goals and revenue workflows You will embed deeply with customers. Part consultant, part engineer, part strategic advisor: leading AI sessions, configuring AI-driven workflows using DealHub MCP, training users on best practices, and ensuring measurable business outcomes. You'll collaborate closely with Customer Success, Product, Support, and Engineering teams, and will be the go-to expert for any AI-related technical solution. This is an ideal role for someone who thrives at the intersection of cutting-edge AI technology and real-world business challenges. This role is ideal for someone who thrives on ambiguity, moves fast, and finds equal satisfaction in writing clean code and presenting a solution to a CFO. Key Responsibilities
* Embed deeply with customers to understand their technical environment, business processes, and AI workflows and then design and build solutions that fit.
* Build and deploy AI-powered workflows, MCP-based automations, and intelligent features within the DealHub platform tailored to each customer's use case taking into account the integration landscape such as Salesforce, HubSpot, Dynamics, NetSuite, and billing platforms.
* Leverage LLM, AI, APIs, webhooks, data pipelines, automation scripts.
* Lead technical AI sessions — review requirements, demo solutions, help build agents and perform training.
* Translate complex AI and technical concepts into clear, tangible business value; operate as a trusted advisor, not just an implementer.
* Identify and resolve technical blockers proactively; operate with confidence under ambiguity and incomplete information.
* Build reusable engineering assets: integration libraries, prompt templates, and AI workflow scaffolding that scale across the customer base.
* Act as a direct feedback loop from the field to the Product and Engineering teams surfacing patterns, edge cases, and AI feature opportunities discovered during deployments.
* Monitor post-go-live and drive continuous improvement.

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.