Machine Learning Expert

Requirements:

• Minimum of 8+ years of experience in Machine Learning Engineering, Applied Machine Learning, or a related field, with a proven track record of building and maintaining production models.
• MLOps and AWS: Expert proficiency with the AWS ecosystem for MLOps, including a deep understanding of how to architect solutions using key services like Amazon SageMaker, S3 AWS Step Functions, AWS CloudFormation, Amazon CloudWatch, Amazon Managed Streaming for Apache Kafka (MSK), and Amazon Bedrock.
• Technical Expertise: Deep expertise in building and deploying scalable solutions for NLP, including experience with challenges such as sarcasm detection, polysemy, and managing multilingual data.
• Experience with a variety of ML algorithms and models, including traditional supervised and unsupervised learning, deep learning, and modern Generative AI techniques (e. g., LLMs, RAG, Prompt Engineering). Proficiency with ML frameworks and libraries such as PyTorch, TensorFlow, and scikit-learn, with an ability to adapt and tune open-source or pre-trained models.
• Software Engineering and Observability: A strong understanding of core software engineering principles, including design patterns, data structures, testing, security, and version control. Experience with continuous integration (CI/CD) and regression testing. You should be able to apply model observability practices for faster issue detection and root cause analysis.
• Problem-Solving: The ability to translate complex business problems into viable technical solutions and communicate findings to stakeholders in non-technical terms.
• Software Engineering: A strong understanding of software engineering principles, including design patterns, data structures, testing, security, and version control.