AI and Data Science Team Lead

Description

About AllCloud

AllCloud is a leader in amplifying organizations’ cloud potential through AI. With a track record of hundreds of successful migrations and implementations across AWS and Salesforce, AllCloud has developed strategies and solutions that enable businesses of all sizes to remain at the forefront of innovation.

AllCloud is a leader in AI-led professional and managed services. As an AWS Premier and audited managed services Partner, and Salesforce Consulting partner, AllCloud provides comprehensive AI-led cloud journey support, from initial migration to ongoing management through our Engage Managed Services. Our expertise ensures that clients remain aligned with ecosystem best practices while focusing on their core business growth.

AllCloud serves clients across the globe with offices in EMEA and North America. www.allcloud.io


About the Role

We are seeking a visionary and hands-on AI and Data Science Team Lead to head our advanced analytics and AI engineering group. Over the past 7 years, our data infrastructure and customer needs have evolved from foundational data engineering and cloud migrations to cutting-edge Generative AI and Machine Learning solutions.

You will lead a high-performing team of AI Engineers and Data Scientists, taking ownership of the technical direction, architecture, and deployment of production-grade AI systems on AWS. The ideal candidate combines a solid foundational background in complex data architecture with state-of-the-art expertise in Gen AI, RAG pipelines, graph-based reasoning, and Enterprise MLOps. You will work closely with enterprise customers across various sectors (manufacturing, banking, healthcare, and high-tech) to translate business constraints into scalable architectures and ship systems that deliver measurable business impact.

Responsibilities

  • Team Leadership: Recruit, mentor, and manage a team of Data Scientists and AI Engineers, driving engineering excellence, technical direction, and best practices.

  • System Architecture & Gen AI: Design and deploy end-to-end AI systems, including large-scale Retrieval-Augmented Generation (RAG) pipelines for technical documentation, Text-to-SQL GenBI agents, and advanced AI document processing systems (OCR, semantic extraction).

  • Enterprise MLOps: Design and deliver secure, enterprise-grade MLOps platforms on AWS (including CI/CD pipelines, feature stores, and monitoring) to dramatically reduce model deployment times.

  • Advanced Data Modeling: Utilize graph embeddings (e.g., Neo4j) and build graph-based AI agents to uncover hidden behavioral patterns, improving LLM reasoning while optimizing token costs and latency.

  • Customer & Stakeholder Collaboration: Work directly with enterprise customers to understand business constraints, define architectural standards, and implement governance guardrails and validation layers for safe enterprise adoption and data protection.

  • Hands-On Execution: Operate dynamically in both startup and enterprise contexts, balancing the need to ship value quickly with maintaining strict production rigor.

Requirements

  • Experience: 7+ years of proven experience designing, deploying, and managing production-grade Machine Learning and AI systems (with a strong foundation in data engineering and scalable pipelines).

  • Leadership: Demonstrated experience leading, managing, and mentoring data science or AI engineering teams.

  • Cloud & Infrastructure: Deep architectural expertise in the Amazon Web Services (AWS) ecosystem, with a track record of deploying large-scale solutions. Experience defining architectural standards to achieve milestones like the AWS ML Competency is a significant advantage.

  • Gen AI & Search: Extensive hands-on experience with Large Language Models (LLMs), deep learning frameworks, domain-specific prompting, and vector search/Elasticsearch.

  • Graph Technologies: Strong background in modeling complex data structures using Graph Databases (e.g., Neo4j) to build scalable pipelines for high-volume data.

  • Education: M.S. (or equivalent) in Engineering, Mathematics, Computer Science, or a related quantitative field.

  • Communication: Excellent interpersonal skills with the ability to lead customer-facing engagements and translate complex technical concepts into business value.

  • Fluent in Hebrew and English


AllCloud is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics, or any other basis forbidden under federal, provincial, or local law.

Summary

Lead AI engineers and data scientists building production-grade generative AI systems, RAG pipelines, MLOps platforms, and graph-based agents on AWS. Requires 7+ years of ML/AI production experience, AWS architecture expertise, LLM and graph database skills, team leadership, and fluency in Hebrew and English.

Responsibilities

Recruit, mentor, and manage Data Scientists and AI Engineers; design and deploy end-to-end AI systems including RAG pipelines, GenBI agents, and document processing; deliver enterprise MLOps and CI/CD on AWS; build graph-based AI agents; collaborate with enterprise customers; balance fast delivery with production rigor

Qualifications

7+ years designing, deploying, and managing production-grade ML and AI systems; demonstrated leadership and mentoring of data science or AI engineering teams; deep AWS architectural expertise; extensive hands-on experience with LLMs, deep learning, vector search/Elasticsearch; Neo4j graph modeling; M.S. or equivalent in a quantitative field; fluent Hebrew and English

Education requirements

M.S. (or equivalent) in Engineering, Mathematics, Computer Science, or a related quantitative field

Experience requirements

7+ years of proven experience designing, deploying, and managing production-grade Machine Learning and AI systems, with a strong foundation in data engineering and scalable pipelines; demonstrated experience leading, managing, and mentoring data science or AI engineering teams; deep AWS architectural expertise; experience with LLMs, vector search/Elasticsearch, and graph databases; fluent Hebrew and English