As a quickly growing company with "Great Place to Work" and "Great Place to Work IT" certified culture rewarded as one of the Best Employers in Turkey; Bentego is always looking for passionate, talented and creative teammates to join our team.

We are looking for an experienced Senior Data Scientist & AI Engineer to design and deliver end-to-end data science, machine learning, and generative AI (GenAI) solutions for our enterprise clients while strengthening our team's technical capabilities. In this role, you will own the full lifecycle, from exploratory data analysis and feature engineering to machine learning and deep learning models, LLM-based solutions, and MLOps processes, combining traditional data science with GenAI and optimization approaches to deliver measurable business value!

Who you are?

Bachelor's degree in Computer Engineering or a related field.
At least 6 years of experience in data science or machine learning, preferably in consulting or project-based environments.
Advanced proficiency in Python and libraries such as NumPy, Pandas, scikit-learn, TensorFlow, and PyTorch. Experience with SQL.
Knowledge of and experience in machine learning (supervised/unsupervised learning, ensemble methods) and deep learning (CNN, RNN, Transformer architectures).
Hands-on experience with LLM, NLP, or generative AI technologies, including prompt engineering, fine-tuning, RAG, embedding models, and vector databases, as well as working with large language models in production.
Experience developing AI agents with tool/function calling, memory, planning, and multi-agent orchestration.
Experience with frameworks, SDKs, and APIs such as LangChain/LangGraph, LlamaIndex, CrewAI, AutoGen, OpenAI Agents SDK, Claude Agent SDK, OpenAI API, Hugging Face, or similar technologies.
Knowledge of Model Context Protocol (MCP).
Knowledge of MLOps tools such as MLflow, Airflow, and Kubeflow, as well as model versioning, CI/CD, and monitoring processes.
Knowledge of version control with Git and container technologies such as Docker and Kubernetes.
Experience providing technical leadership or mentoring to a team, and designing and delivering workshops and training.

Nice to have:

A master's degree or PhD in artificial intelligence, machine learning, data science, or a related field.
Experience in model and inference optimization, including quantization, distillation, pruning, batching, caching, and GPU resource planning.
Experience in hyperparameter and pipeline optimization, including Bayesian optimization and genetic algorithms.
Experience solving operational optimization problems using linear/integer programming and constraint-based optimization, and integrating these approaches with GenAI solutions.
Project experience in banking, insurance, telecommunications, aviation, or manufacturing.
Experience in explainable AI (XAI) and model governance within regulated industries.
Experience with distributed data technologies and file systems, including Spark, Hive, Hadoop/HDFS, and Dask.

What You Will Do?

Perform exploratory data analysis (EDA) to understand data structure and assess data quality and accuracy; use Jupyter Notebook for rapid analysis and prototyping.
Carry out data cleaning, preparation, and feature engineering; build reusable pipelines.
Develop machine learning and deep learning models across all stages, including design, training, evaluation, validation, and deployment.
Formulate and solve optimization problems that translate model outputs and statistical findings into actionable decisions.
Measure the business impact of models and decisions through experimental design, including A/B testing.
Design and deploy AI agents, agentic workflows, and reusable agent skills.
Build LLM-based GenAI solutions.
Select commercial and open-source language models based on use case requirements.
Establish and manage MLOps/LLMOps processes, including model versioning, CI/CD, monitoring, and deployment.
Integrate models into business processes and enterprise systems through APIs.
Collaborate with business teams to identify use cases.
Communicate findings to non-technical stakeholders through data visualization and clear presentations.
Provide technical leadership and mentoring; design and deliver workshops and training to strengthen the team's capabilities.