AI/ML GCP Engineer

Endava

Warsaw, Poland

Job Summary

We are looking for a Machine Learning Engineer (AI/ML) with 3-5 years of experience to develop and deploy scalable AI models. The ideal candidate has hands-on experience with machine learning algorithms, deep learning frameworks, and cloud-based ML solutions. You will work on real-world AI applications, optimize ML models, and collaborate with data scientists, software engineers, and product teams to deliver AI-powered solutions.

Key Responsibilities

Develop, train, and deploy ML models for applications in NLP, computer vision, recommendation systems, or predictive analytics.
Design and implement ML pipelines for model training, validation, and deployment.
Preprocess and analyze large datasets to extract meaningful insights and improve model performance.
Optimize and fine-tune ML models using techniques like feature engineering, hyperparameter tuning, and distributed training.
Deploy ML models in production using MLOps best practices with cloud platforms (GCP, AWS, or Azure).
Collaborate with cross-functional teams to integrate AI models into production systems.
Monitor and maintain deployed models, ensuring their performance and retraining when necessary.
Stay up-to-date with industry trends, emerging AI/ML technologies, and best practices.

Qualifications

Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
3-5 years of experience in machine learning, AI, or data science roles.
Strong programming skills in Python (TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
Experience in building and deploying ML models on cloud platforms (Google Cloud Platform, AWS, or Azure).
Hands-on experience with ML algorithms (classification, regression, clustering, reinforcement learning).
Familiarity with deep learning architectures (CNNs, RNNs, Transformers, GANs).
Understanding of MLOps practices (CI/CD pipelines, model monitoring, and retraining).
Experience working with large-scale datasets using SQL, BigQuery, or Spark.
Knowledge of containerization and orchestration using Docker and Kubernetes.

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