We are looking for a highly skilled and experienced Senior Machine Learning Engineer to join our dynamic team. The ideal candidate will bring a strong background in DevOps (ML Ops), software engineering, and data science, combined with advanced cloud engineering expertise. This role is essential for designing, deploying, and managing machine learning models in production environments, contributing to cutting-edge solutions that drive business impact. The ideal candidate should have a keen eye for detail, exceptional problem-solving skills, and the ability to collaborate effectively across teams.

Responsibilities

• Lead and manage the end-to-end deployment, monitoring, and maintenance of machine learning models in production.
• Apply software engineering best practices to build robust ML pipelines and integrate data science methodologies for effective model development and validation.
• Design, develop, and optimize scalable and secure cloud-based architectures, leveraging Azure services for ML model deployment and operations.
• Use Terraform to automate infrastructure setup and management, ensuring seamless integration and deployment of ML solutions.
• Work closely with data engineers, software developers, and cross-functional teams to design and implement data-driven solutions.
• Proactively identify and resolve issues in model performance, production environments, and deployment workflows.
• Maintain comprehensive documentation and develop best practices for model deployment, monitoring, and lifecycle management.

Qualifications

• Significant experience in managing ML pipelines, CI/CD for ML models, and maintaining model performance in production.
• Expert-level knowledge of cloud platforms, particularly Azure, and experience with scalable, cloud-based ML solutions.
• Advanced proficiency in Python for model development and scripting, SQL for data manipulation, and Terraform for infrastructure as code.
• Hands-on experience working with data platforms and technologies such as Data Bricks and Snowflake for data storage and processing.
• Demonstrated ability to deploy, monitor, and optimize machine learning models in production environments.
• Excellent verbal and written communication, capable of clearly articulating complex concepts to technical and non-technical audiences.

Preferred Qualifications

• Experience with both supervised and unsupervised learning algorithms, model training, tuning, and evaluation.
• Familiarity with generative AI concepts, including large language models (LLM) and their applications.
• Knowledge of semantic search technologies and vector databases for building advanced AI search solutions.
• Proven experience in writing clear technical documentation for deployment strategies and processes.

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