Senior ML Engineer – Cloud-Native AI/ML Solutions
🇽🇰 Prishtina, Kosovo
EngineeringAI/MLSenior
Compare your skills with our requirements
We are seeking a senior-level ML Engineer with a proven track record in delivering production-grade AI/ML systems. You should have hands-on experience building and operationalizing ML solutions in AWS, covering the full lifecycle from data ingestion and pipelines to training, deployment, and monitoring.
Strong skills in Python, TensorFlow, PyTorch, and Scikit-learn are essential, alongside practical knowledge of data engineering tools like Spark, Pandas, and NumPy. You should also be comfortable designing and deploying deep learning and classical ML models for real-world use cases.
Practical experience with AWS AI/ML services (SageMaker, Bedrock, Lambda, Step Functions, CloudWatch) is a must, and familiarity with Databricks, MLflow, Terraform, and CI/CD pipelines will make you stand out.
If you have worked with Generative AI applications, LLM fine-tuning, or event-driven architectures, you’ll bring an additional edge to the team.
Must-have
- Proven ability to architect and operationalize AI/ML solutions in AWS (SageMaker, Lambda, Step Functions, CloudWatch).
- 5+ years of professional experience in machine learning engineering.
- Strong expertise in Python, TensorFlow, PyTorch, Scikit-learn, and XGBoost.
- Experience with data preprocessing, feature engineering, and model evaluation.
- Hands-on deployment of ML models in production using Amazon SageMaker (including containerization).
- At least one relevant certification: Databricks Certified Machine Learning – Associate | Databricks Certified Generative AI Engineer – Associate | AWS Certified Machine Learning Engineer – Associate
Is a plus
- Advanced certification (e.g., Databricks ML – Professional, AWS ML – Specialty).
- Experience with Generative AI (Amazon Bedrock, Anthropic, custom LLMs).
- Exposure to event-driven architectures and IoT-powered automation.
- Knowledge of MLOps best practices with MLflow, Terraform, and CI/CD workflows.
- Strong background in Databricks model management and data lake architectures.
What you will do
- Architect and operationalize end-to-end AI/ML solutions in AWS, integrating scalable data pipelines, model training, deployment, monitoring, and governance using services like SageMaker, Lambda, Step Functions, and CloudWatch.
- Design and implement ML models for classification, regression, clustering, and recommendation systems.
- Develop and optimize deep learning architectures using TensorFlow and PyTorch.
- Perform data preprocessing, feature engineering, and model evaluation with advanced ML/statistical techniques.
- Deploy ML models into production using Amazon SageMaker, leveraging containerization and automated pipelines.
- Design and deploy Generative AI applications using Amazon Bedrock, integrating foundation models (Anthropic, AWS, and others).
- Train, fine-tune, and deploy custom LLMs using SageMaker JumpStart and SageMaker Pipelines.
- Manage model lifecycle and governance with SageMaker Model Registry and Databricks Model Registry to ensure traceability and reproducibility.