AI and ML Engineer

DataArtArtificial intelligence

Workplace type: remote; Country code: PLFull-TimeSenior

Salary23,000 - 27,700 PLN per month

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Job Details

Languages: En B2
Experience: 5+ years of professional experience in machine learning, AI engineering, or related fields.
Required Skills: AWSPythonGitPyTorchSparkTensorflowAWS Lambdascikit-learnMLOps

Have 5+ years of professional experience in machine learning, AI engineering, or a related field.
Hold a master’s degree in Computer Science, Machine Learning, Physics, or a related field; a PhD is preferred.
Be proficient in Python and machine learning frameworks including TensorFlow, PyTorch, and scikit-learn.
Have a strong understanding of algorithms, statistics, probability, and linear algebra.
Have hands-on experience with data pipelines and ETL tools such as Spark, AWS Lambda, or AWS Glue.
Have practical experience with cloud platforms, preferably AWS.
Have software engineering fundamentals including version control with Git, testing, and design patterns.
Have demonstrated success deploying machine learning models into production at scale.
Be familiar with MLOps tools such as MLflow, Kubeflow, SageMaker, or Vertex AI.
Have analytical, problem-solving, and communication skills.
Be able to work autonomously and collaboratively in a cross-functional team environment.
Experience with agentic frameworks such as LangChain or LangGraph is a nice-to-have.

Design, implement, and optimize machine learning and AI models, including classification, regression, clustering, and agent tuning.
Experiment with methods, frameworks, and architectures to enhance model performance and efficiency.
Develop scalable ML pipelines for training, validation, and inference.
Deploy ML models into production environments, ensuring reliability, low latency, and scalability.
Apply MLOps practices including CI/CD, monitoring, automated retraining, and model registry management.
Partner with data engineering teams to source, clean, and transform datasets for model training and inference.
Ensure data quality, perform feature engineering, and support real-time data integration.
Work with data scientists, software engineers, and product managers to align ML solutions with business goals.
Communicate technical results to technical and non-technical stakeholders.
Provide technical guidance and mentorship, and contribute to team practices, code reviews, and architectural decisions.

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