AI Engineer (All Experience Levels Welcome) - (Bucharest | Hybrid)

AI Engineer (All Experience Levels Welcome)

About the Role

We are looking for an AI Engineer with hands-on experience in machine learning, data science, or applied AI development. Whether you're early in your career or bring several years of experience, you are welcome to apply.

In this role, you will help build modern AI solutions, experiment with LLMs and agentic AI frameworks, and translate business needs into practical prototypes and insights. You will work closely with senior AI engineers while taking ownership of modules, proofs of concept, and analytical tasks, with opportunities to grow into more complex areas of AI engineering.

Key Responsibilities:

AI & ML Development

• Develop, test, and optimize machine learning models and AI agents using Python.
• Contribute to building LLM-based solutions, including RAG workflows, embeddings, prompt engineering, and evaluation.
• Support the development of agentic AI systems, leveraging frameworks such as LangChain, LangGraph, or other Agent SDKs.
• Build early-stage prototypes and proofs of concept (PoCs), transforming exploratory ideas into functional solutions.
• Integrate models or agents into applications and pipelines, collaborating closely with senior engineers.

Rapid Prototyping

• Use rapid prototyping tools such as Lovable, low-code/AI builders, or internal sandbox environments to quickly validate ideas.
• Participate in iterative design cycles, helping move from concept prototype MVP.
• Collaborate with stakeholders to experiment, test, and refine AI-driven features at high speed.

Data Analysis & Preparation

• Collect, clean, and transform datasets from various sources to ensure high-quality model inputs.
• Conduct exploratory data analysis (EDA) to uncover patterns, test hypotheses, and support data-driven decision-making.
• Prepare clear documentation of datasets, model behavior, and experimental findings.

Collaboration & Communication

• Work with cross-functional teams to translate business challenges into AI tasks and tangible outcomes.
• Create visualizations and analytical summaries using tools like Plotly or Matplotlib.
• Present results and insights clearly to technical and non-technical audiences.

Tools & Platforms

• Use Git, Jupyter Notebook, VS Code, and standard ML development tools.
• Work within data and AI platforms including Dataiku and Snowflake Cortex/Intelligence - with Databricks being added to our offering.
• Explore GenAI platforms, agent frameworks, and low-code AI tools such as Microsoft Copilot Studio.

Key Requirements:

Education & Background

• Bachelors or Masters degree in Computer Science, Data Science, Engineering, Statistics, or a related field.
• All experience levels are welcome, from early career to more experienced profiles.

Technical Skills

• Strong Python skills for data manipulation and model development.
• Working knowledge of SQL.
• Experience with ML libraries such as scikit-learn, pandas, NumPy.
• Familiarity with GenAI concepts including LLMs, embeddings, RAG, and prompt engineering.
• Understanding of agentic AI principles, or strong willingness to learn (LangChain, Agent SDK, etc.).
• Experience using Git and standard ML tooling.

Nice to Have

• Experience with cloud platforms such as Dataiku, Snowflake, Databricks, or others.
• Understanding of model monitoring (quality, drift, bias, hallucinations).
• Experience with MLOps pipelines or evaluation frameworks.

Soft Skills

• Ability to communicate insights clearly and convert analytical results into actionable recommendations.
• Strong collaboration skills and willingness to engage with cross-functional stakeholders.
• Curiosity, adaptability, and a proactive approach to learning new tools and emerging GenAI concepts.
• Upper-intermediate English proficiency.

What We Offer

• Mentorship from senior AI engineers and a clear growth path.
• Exposure to cutting-edge GenAI tools, LLM frameworks, and agent-based architectures.
• Opportunities to work on meaningful PoCs, rapid prototypes, and innovative AI initiatives.
• A supportive environment that encourages experimentation, learning, and fast skill development.