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Senior Machine Learning Engineer
2 weeks ago
Senior Machine Learning Engineer
Dubai, UAE (Relocation Required)
Full-time | On-site
About the Role
We are seeking a highly skilled and experienced Senior Machine Learning Engineer to join our team.
This role is ideal for someone with strong expertise in designing, deploying, and managing advanced ML systems with a focus on automation and reliability.
You'll play a key role in shaping the tools, practices, and standards used across the ML lifecycle, collaborating closely with both technical and non-technical stakeholders.
Responsibilities
• Lead the design, development, and operational lifecycle of Machine Learning pipelines, incorporating industry best practices.
• Design, implement, and enhance critical steps in ML pipelines for scalability, efficiency, and reliability.
• Own and drive the design and execution of ML prediction endpoints, ensuring robust performance and alignment with project requirements.
• Collaborate with Systems Engineers to create a reliable ML lifecycle management environment, integrating seamlessly with existing systems.
• Draft technical specifications, documentation, and user guides to support developed applications and workflows.
• Advocate for and support improvements in coding practices and organization within scientific work cycle repositories.
• Build and manage ML pipelines across various projects, ensuring rapid deployment and iterative improvements.
• Identify and assess technical risks and gaps, proposing and implementing risk mitigation strategies.
• Partner with Data Scientists to transition predictive models into production environments, creating scalable data preparation pipelines and understanding model scopes and objectives.
Requirements
Experience:
Minimum 3 years in a Machine Learning Engineer role or a similar position.
•
Programming Skills:
Strong proficiency in Python and related ML libraries(PyTorch,Hugging face, Transformers) .
•**Data Science & ML Knowledge:**Solid grasp of Data Science principles and Machine Learning fundamentals, with hands-on model creation experience. Background in machine learning algorithms, including creating models from scratch
•
Engineering Best Practices:
Extensive experience in implementing engineering best practices and a deep understanding of Machine Learning fundamentals.
•
Data Products & Big Data Technologies:
Hands-on experience with technologies like Apache Spark (Spark SQL, MLlib/Spark ML) or similar big data frameworks, and proficiency in tools such as Hadoop, Kafka, Cassandra, GCP BigQuery, AWS Redshift, Apache Beam, Apache Flink ,etc.
•
Automation & Workflow Management:
Experience with automated data pipeline and workflow tools, such as Airflow, Argo Workflows, Kubeflow ,etc.
•
Data Processing Paradigms:
Familiarity with batch, micro-batch, and streaming data processing paradigms.
•
Cloud Providers:
Practical experience with major cloud providers, including AWS, GCP, or Azure.
•
MLOps Platforms & Tools:
Proficiency in one or more MLOps platforms/technologies such as AWS SageMaker, Azure ML, GCP Vertex AI, Databricks, MLFlow, Kubeflow, or TensorFlow Extended (TFX).
•
Production ML Integration:
Experience in integrating ML models within complex, data-driven systems in production environments.
•
Language Proficiency:
Fluent English, B2+ level or higher.
Preferred Qualifications:
•ML Tools for Advanced Use Cases:Experience with Large Language Models (LLMs) and computer vision applications, including image generation tools, Speech-to-Text (STT), Speech-to-Speech (STS), and Text-to-Speech (TTP).
• Certifications:
AWS, GCP, or Azure certifications are a strong advantage.
•
Adaptability:
Ability to quickly adapt to new technologies and environments, with a startup mindset for handling ambiguity and fast-paced change.
How to Apply
Apply via LinkedIn or send your CV to
