Applied Ai Engineer

Design, develop, and deploy production-ready applications using both traditional machine learning techniques and modern Generative AI models Prepare, clean, and transform large datasets to support machine learning and AI workflows Develop, train, evaluate, and optimize machine learning models through experimentation and hyperparameter tuning Design and implement retrieval-augmented generation (RAG) pipelines and AI-driven automation workflows Analyze model performance using appropriate metrics and validation strategies, and continuously improve models based on results Implement backend services and APIs to integrate AI capabilities into scalable systems Collaborate with cross-functional teams to translate business requirements into AI-enabled solutions Contribute to software quality through automated testing, code reviews, and engineering best practices What you need to have experience in:
Professional experience with Python and taking AI to production Experience with the machine learning lifecycle, including data preparation, feature engineering, model training, evaluation, and optimization Hands-on experience with common Python ML and data libraries such as pandas, NumPy, scikit-learn, or similar Familiarity with Generative AI concepts such as LLMs, embeddings, or retrieval-augmented generation (RAG) Experience working with structured and unstructured data processing pipelines Experience designing and building RESTful APIs and backend services Experience with CI/CD pipelines and modern development workflows Familiarity with Agile methodologies Familiarity with AI-assisted development tools and modern IDEs Strong written and verbal communication skills in English It would be a plus if you also have:
Hands-on experience deploying AI systems on AWS, particularly with services such as Amazon Bedrock Agent Core, AWS Lambda, Amazon SageMaker, Amazon S3, and Amazon API Gateway Experience designing secure, scalable architectures within AWS (IAM, VPC, CloudWatch) Familiarity with software testing practices and frameworks (e.G., pytest or similar) Knowledge of MLOps practices, experiment tracking, and model monitoring Experience with Docker, Kubernetes, or similar containerization/orchestration technologies Exposure to fine-tuning approaches or parameter-efficient training methods Backend engineering experience with Java or similar strongly typed languages What we offer
Flexible working environment
We look after our team's quality of life.
Our focusis to support you by balancing your needs and ourcustomers demands.
Health insurance
We have a health insurance company plan to getyou covered.
Continuous learning
We have a learning budget available for you and westimulate sharing knowledge between ourteam.
3 extra holidays per year
We offer 25 paid holidays to all members of our team.
Innovative environment
We are young and innovative with a team focused ondelivering excellence Gifts & perks
We usually have some surprises for the entireteam.