Applied Ai Engineer
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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.
#J-*-Ljbffr
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