דרושים » AI » AI Growth Lead

We're looking for an AI Builder to join our Marketing team- someone who will design, build, and ship AI-powered tools and workflows that make marketing faster, smarter, and more effective. This role reports to the VP of Marketing.
If you're a creative, self-driven builder who moves seamlessly from ideation to execution, this role was made for you. You thrive in dynamic, fast-moving environments, take full ownership of your projects, and don't need hand-holding to turn an idea into a working tool. You'll be the team's go-to AI owner - collaborating closely with marketing stakeholders while also pursuing your own ideas, embracing a fail-fast mindset to try, scale, or kill initiatives quickly.
What will you do?
Own marketing AI tools: Fully own, build, and maintain all AI-driven tools that support the marketing team.
Lead lead-generation initiatives: Drive growth-hacking and meeting-generation pipelines end to end.
Build inbound demand: create lead magnets, interactive AI-powered assets, an SEO/AEO content engine, and on-site personalization that turn visitors into pipeline.
Build outbound demand: own list building, data enrichment, intent-signal monitoring, and 1:1-at-scale outbound sequences that create net-new pipeline.
Run an AI creative pipeline: produce video, image, and copy at volume with AI tools, giving marketing a scalable creative engine.
Educate the team on AI: Teach and keep the marketing team up to date on the latest AI trends and capabilities.
Build collaborative and independent tools: Partner with the team to build impactful day-to-day tools while also pursuing independent AI initiatives.
Unblock the AI wishlist: Address the marketing team's backlog of AI ideas that they haven't had time to implement.
Experiment and iterate quickly: Apply a fail-fast approach, testing new tools and initiatives, then scaling or killing them based on results.

Required AI Data Engineer
About the role:
We're building the intelligence layer that lets everyone ask hard questions of our data and get trustworthy answers - in plain language, in seconds. As AI Data Engineer, you own the semantic and AI-native surface of our Snowflake platform: the governed semantic layer that defines "what a metric means," the Cortex Agents that let business users query it conversationally, and the infrastructure that keeps all of it fast, reliable, and ready to scale.
This is a builder-owner role. You'll ship the semantic models, wire up the agents, and set the standard for how analysts across the company work with data. You'll also lead the Data Analyst guild - the connective tissue that keeps our hub-and-spoke analytics model coherent as it grows.
If you're excited by the space where data engineering, LLM tooling, and analytics governance meet, this role sits right in the middle of it.
What youll Own:
Snowflake semantic layer - Own the semantic layer end-to-end as the single source of truth for metrics; design semantic models, enforce naming standards, and ensure consistent metric definitions across dashboards and AI agents.
Cortex Agents - Design and deploy conversational AI agents using Cortex Analyst and Cortex Search; tune for accuracy and safety, expose through multiple surfaces (Snowflake Intelligence, Streamlit, MCP), and build evaluation harnesses to maintain quality at scale.
Data craft (Analytics guild) - Co-lead the technical track of the Analytics guild; set SQL and modeling standards, run technical enablement and code reviews, and serve as the technical authority for analysts.
Scaling data infrastructure - Improve performance, reliability, cost efficiency, and governance across the dbt/Airflow/Airbyte/Snowflake stack as data volume and query load grow; optimize warehouse sizing, medallion layers, and ingestion pipelines.
What you'll do day to day:
Model and maintain semantic views that power both dashboards and AI agents, keeping definitions versioned, tested, and certified.
Build, evaluate, and iterate on Cortex Agents - including retrieval quality, guardrails, and observability.
Extend and optimize dbt models, Airflow DAGs, and Airbyte connectors across bronze/silver/gold layers.
Partner with GTM, Finance, CS, and Product stakeholders to translate business questions into governed, reusable data assets.
Run the Data Analyst guild: standards, reviews, enablement, and tooling.
Own data quality, lineage, and cost monitoring across the Snowflake platform.
Expose data and agents through Streamlit apps, BI tools (Omni), and MCP servers for internal AI workflows.

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.

You will manage the AI Platform Engineer(s), set the technical standards for the AI Power User group's citizen development program, and serve as the connective tissue between business leadership, platform owners, and development teams. You will shape the multi-year AI architecture roadmap while also rolling up your sleeves to conduct architecture reviews, resolve blockers, and move use cases from concept to production. This is a role for someone who can think big and execute - and who understands that in an enterprise context, the quality of your governance is inseparable from the quality of your architecture.

What You'll Own
Strategy & Architecture
Define and own the enterprise AI integration strategy - identifying opportunities to embed intelligent automation, agentic workflows, predictive analytics, and generative AI capabilities across our company core platforms
Develop and maintain reference architectures, design patterns, and the AI architecture decision log that governs how AI models connect to enterprise systems and what they are permitted to do
Consult on enterprise system architecture and implement best practices for the Enterprise Business Systems team to leverage in their day-to-day execution.
Lead Proof-of-Concept initiatives for new AI tools and platform-native AI features, evaluating them against build-vs-buy criteria before recommending adoption
Partner with business stakeholders to translate operational pain points into AI use cases with clear ROI framing and sequencing criteria
Contribute to our enterprise data strategy, ensuring AI initiatives are supported by clean, accessible, and well-governed data pipelines
Integration Architecture & Delivery

Design and own the Workato eMCP layer - the MCP governance model, persona-scoped token framework, workspace isolation strategy, and the single sanctioned action surface through which all AI agents write back to enterprise systems
Define integration patterns and standards for AI model connectivity (Claude, ChatGPT) to Salesforce, NetSuite, HiBob, and Jira - specifying what agents can read, what they can write, through which surfaces, and with what confirmation and audit requirements
Design and oversee API strategies, event-driven architectures, and middleware patterns that support scalable AI feature delivery - including agentic workflows, intelligent data transformation, anomaly detection, and natural language interfaces layered onto ERP and CRM data
Collaborate with Engineering during build phases, conducting architecture reviews, providing hands-on guidance, and resolving complex technical blockers
Define non-functional requirements - latency, security, auditability, model drift monitoring - for AI components embedded in mission-critical business processes
Establish MLOps and LLMOps practices appropriate for our enterprise environment: model versioning, observability, and rollback procedures for production AI workloads

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

We're looking for a Senior AI Product Manager to take ownership of core areas of our B2B platform - the point where powerful AI capabilities become real products that customers rely on. We've already built the foundation: rich data, agentic infrastructure, and deep domain intelligence. Now we need someone who can take what's working with one customer and make it work everywhere - productizing capabilities, scaling them across our customer base, and raising the quality bar as they grow.

This is a hands-on builder role. You'll work in close, direct partnership with both customers and R&D - running discovery, analyzing the data, and prototyping ideas yourself, then teaming up with R&D to bring them to life at scale.

This is a fast-moving area, and we'll shape the exact scope together, based on where you can create the most leverage for the business. We're looking for true startup reflexes: the ability to shift focus as priorities shift, zero in on what matters most this quarter, and make clear, deliberate trade-offs.

If you thrive in a fast-paced startup environment and want to build AI products that make complex insights accessible and actionable for a real-world, >$1B industry, this role is for you.

What You'll Do

Own core areas end to end - strategy, discovery, execution, and measurement. Define what success looks like in numbers, and stay accountable to it well beyond launch.
Productize and scale what works. Turn capabilities proven with one customer into products that work seamlessly across your entire customer base.
Lead discovery yourself. Run customer and prospect conversations to uncover what people will actually pay for, and bring back a scoped, evidenced bet.
Prototype your ideas. Go from concept to working prototype using coding agents and AI tooling, with a design bar high enough to serve as a real proposal.
Own quality. Define what "good" means for our agents, build the evals to measure it, and raise the bar on reliability, cost, and trust as the system evolves.
Partner deeply with R&D. Engage on technical trade-offs - accuracy, latency, cost, build vs. buy - and earn the team's respect through substance.
Drive it to market. Team up with design, sales, marketing, and customer success to package, position, and launch it, then feed adoption data back into the roadmap.

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
Responsibilities:
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 looking for a Senior Software Engineer to join the AI Engineering team in a role focused on embedding directly with business teams to drive AI transformation. Be at the forefront of Applied AI. This role is outward-facing: you will engage with GTM and G&A functions - Sales, Marketing, Finance, HR, Legal, Customer Success - and fundamentally alter how these teams operate with AI.

What you will be doing:
Embed with GTM and G&A teams (Sales, Marketing, Finance, HR, Legal, Procurement, Customer Success) to identify, scope, and prioritize the highest-impact AI opportunities within each function, and act as both product manager and engineer for these initiatives.
Own the full lifecycle of each solution, from requirements gathering through deployment to adoption measurement and iteration, with a focus on business outcomes.
Serve as the primary technical partner and AI advisor for business function leaders, translating their needs into engineering plans and helping them develop their own AI fluency.
Develop and deliver hands-on AI enablement and training for non-R&D teams, building their ability to use AI tools independently and effectively.
Collaborate with the team to leverage shared infrastructure (MCP servers, RAG systems, evaluation frameworks, guardrails) while feeding back requirements from the field.
Measure and report on AI adoption impact - unlocking new opportunities, process improvements, and capability gains - to build the case for continued investment and to guide prioritization.