Data Scientist (AI Engineer)

Tel Aviv
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist - Measurement Specialist

Data Scientist (Predictive Modelling) – NHS

Data Science Placement Programme

Data Science Placement Programme

Data Scientist (AI Engineer)
Salary dependent on skills and experience plus share options in a hyper-growth startup.
Hybrid – office in Central Tel Aviv.
The Company
Streamlining business development for Dealmakers.
This SaaS startup aims to tackle inefficiencies, disconnected systems, and missed opportunities to help organisations grow smarter and faster. With innovation at its core, the platform strives to simplify business growth using cutting-edge AI technology.
The Role
Join a small, dynamic, and growing team of experienced professionals dedicated to revolutionising business development. As the Founding AI Engineer, you will work closely with the co-founders to rapidly iterate on product ideas and help shape the technical vision from the ground up. This is a hands-on role that requires deep technical expertise, entrepreneurial drive, and the ability to turn ideas into scalable solutions.
You’ll have the opportunity to build the product from the earliest stages, solving real customer problems, and playing a key role in the company’s journey towards achieving its business milestones.
Key Responsibilities but not limited to:

  • Collaborate with cross-functional teams to identify business opportunities and design data-driven solutions to address them.
  • Develop, fine-tune, and deploy machine learning and deep learning models, including neural networks, that enhance the platform’s insights and intelligence.
  • Integrate large language models (LLMs) into our product pipeline, exploring cutting-edge techniques such as Retrieval-Augmented Generation (RAG) for improved data insights and user interaction.
  • Leverage LLM frameworks like LangChain to build and manage LLM-based workflows, adapting pipelines to respond to evolving data and user needs.
  • Perform exploratory data analysis, data processing, and feature engineering to support model building.
  • Partner with engineering teams to integrate data solutions into the product, ensuring scalable and reliable deployment.
  • Create and manage data pipelines, ensuring data integrity, quality, and compliance with industry standards (SOC 2, ISO 27001).
  • Conduct experiments, validate hypotheses, and iteratively improve models based on real-world feedback.
  • Communicate findings and insights to non-technical stakeholders to inform decision-making.
  • Establish best practices in data science, data science, ML/AI, and deep learning, setting standards for a growing data team.
    Key Skills:
  • Fluency in English.
  • Minimum 3-5 years of professional or academic experience in data science, machine learning, AI, deep learning etc.
  • Strong proficiency in Python, with experience using machine learning and deep learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
  • Familiarity with key machine learning algorithms, including but not limited to decision trees, gradient boosting, clustering, and neural networks for complex data modelling.
  • Practical experience deploying AI/ML models, including LLMs, using techniques such as RAG, fine-tuning, and prompt engineering.
  • Familiarity with LLM pipelines and frameworks such as LangChain to enhance product capabilities and model integration.
  • Strong experience with data analytics, statistical modelling, and predictive analytics.
  • Solid understanding of SQL and experience with relational and non-relational databases; familiarity with cloud data solutions (e.g., AWS Redshift, Azure Synapse) is a plus.
  • Experience working with large datasets and data pipeline frameworks (e.g., Spark, Airflow).
  • Knowledge of cloud platforms (AWS, Azure) and scalable infrastructure for ML, deep learning, and LLM pipelines.
  • Experience with LLMs, NLP, LLM techniques such as RAG.
  • Bonus: Interest or experience in M&A, finance or business strategy.
  • An entrepreneurial mindset with a passion for using data to drive innovation and solve real business challenges.
    Why Join us?
    This role is ideal for someone who is excited about building a product from the ground up, working in a fast-moving startup, and solving real customer problems. You'llhave the opportunity to grow within the company as we scale and make a meaningful impact on the future of dealmaking.
    Interested? If you feel that you possess the relevant skills and experience, then please submit your CV.
    INDHS

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.