Data Science / AI Engineer

Ness Digital Engineering

About The Position

We are seeking a Data Scientist with an interest in Generative AI, LLMs, Machine Learning, and agentic systems. This role is part of a newly established Advanced Solutions team focused on applied AI research, rapid prototyping, and turning innovative ideas into real business solutions. Working in a dynamic, startup-like environment, you'll receive high-level challenges and collaborate with the team to quickly design, build, test, and iterate on AI solutions. We're looking for someone who enjoys experimenting, learning new technologies, and taking ownership from idea to implementation.

Requirements

Data Scientist / AI Engineer / Machine Learning Engineer
Around 2–5 years of experience in AI, Machine Learning, or Data Science (seniority is flexible—we're more interested in mindset, hands-on experience, and motivation).
Experience with Python and data processing (PySpark and SQL are an advantage).
Hands-on experience with Machine Learning, Generative AI, LLMs, or Agentic AI through commercial or personal projects.
Understanding of modern AI technologies and willingness to continuously learn and experiment.
Ability to take ownership of tasks and deliver end-to-end solutions.
Detail-oriented with a practical, "getting things done" mindset.
Comfortable working in a fast-moving, startup-like environment with evolving priorities.
Strong analytical and critical thinking skills.
English level: B2 minimum.

Nice To Haves

PySpark and SQL are an advantage.

Responsibilities

Reinvent how insights are shared and consumed by our customers.
High-level orchestration across data ecosystems to eliminate silos.
Hyper-personalization and natural interaction for fleet operations.
Seamless "what if" scenario exploration and data-driven decision making for fleet managers.
Design and build AI components that enhance predictive models for accident risk, energy consumption, tire wear, and other key indicators.
Rapidly prototype, test, and iterate on new AI, Machine Learning, and GenAI solutions.
Work across the full lifecycle—from understanding the problem, preparing data, building prototypes, validating results, and deploying working solutions.
Contribute to long-term innovation by exploring emerging AI technologies, bringing new ideas, and experimenting with modern frameworks and approaches.
Collaborate closely with a small international team to solve open-ended business challenges and deliver practical AI solutions.
Experiment with technologies such as LangChain, LangGraph, MCP, Agentic AI, and other modern AI frameworks where appropriate.
Evaluate different approaches and select solutions based on business value, performance, and scalability.
Contribute across the full lifecycle—from prototype (POC) to industrialization and market launch.