Machine Learning Engineer

·  As a Machine Learning Engineer, selected specialist will take ownership of deploying, operating, and enhancing AI/ML models within production environments. He/she will collaborate closely with data scientists, software developers, and IT stakeholders to ensure smooth integration and reliable performance of machine learning solutions. This role plays a key part in translating AI initiatives into scalable, dependable, and business-driven production systems.

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related discipline.

• Minimum of 4 years of hands-on experience deploying machine learning models into production.

• Solid understanding of machine learning, deep learning, NLP, and generative AI methodologies.

• Experience with MLOps platforms and frameworks such as MLflow, Kubeflow, TensorFlow Extended (TFX), or equivalent.

• Practical knowledge of CI/CD tools including Jenkins, GitLab CI, or CircleCI.

• Strong programming skills in Python and familiarity with ML/DL frameworks such as TensorFlow, PyTorch, and scikit-learn.

• Experience working with cloud providers (AWS, GCP, Azure) and their AI/ML ecosystems.

• Proficiency in containerization and orchestration tools, including Docker and Kubernetes.

• Good understanding of version control systems (e.g., Git) and collaborative development practices.

• Strong analytical and problem-solving abilities, with experience designing scalable MLOps architectures.

• Excellent communication skills and the ability to work effectively within cross-functional teams.

• Fluent English

Nice to have:

• Experience with big data technologies such as Hadoop, Spark, and Kafka.

• Familiarity with data visualization tools like Tableau, Power BI, or similar platforms.

Machine learning, NLP, AI, Tensorflow, MLFlow, Kubeflow, Jenkins, Gitlab, CircleCI, Python, scikit-learn, Cloud, Git

Preferred Technical Skills

Hadoop, Spark, Kafka, Tableu, Power BI

Main Responsibilities

• Architect, build, and maintain comprehensive MLOps pipelines to support end-to-end model deployment.

• Partner with data scientists to gather model requirements and facilitate seamless production integration.

• Develop and oversee infrastructure for model training, testing, deployment, monitoring, and automated retraining.

• Establish and manage CI/CD workflows to enable efficient model releases and updates.

• Continuously monitor deployed models to ensure optimal scalability, stability, and performance.

• Leverage containerization and orchestration technologies (e.g., Docker, Kubernetes) to manage deployment environments.

• Deploy and maintain AI/ML services across cloud platforms such as AWS, GCP, and Azure.

• Apply security best practices to safeguard ML models and data pipelines.

• Diagnose and resolve production issues related to model deployment and operations.

• Keep current with emerging MLOps tools, frameworks, and industry best practices.

• Maintain clear documentation of systems, workflows, and configurations to support knowledge sharing and continuity.