Senior ML Ops Engineer

As a Senior ML Ops Engineer at Epergne Solutions, your role will involve designing data pipelines and engineering infrastructure to support enterprise machine learning systems at scale. You will be responsible for converting offline models developed by data scientists into real machine learning production systems. Your duties will include developing and deploying scalable tools and services for clients to handle machine learning training and inference efficiently. Key Responsibilities: - Design data pipelines and engineering infrastructure for enterprise machine learning systems - Transform offline models into production-ready machine learning systems - Develop and deploy scalable tools and services for machine learning training and inference - Identify and evaluate new technologies to enhance performance, maintainability, and reliability of machine learning systems - Apply software engineering best practices to machine learning, including CI/CD and automation - Support model development with a focus on auditability, versioning, and data security - Facilitate the development and deployment of proof-of-concept machine learning systems - Communicate with clients to gather requirements and track progress - Demonstrate robust analytic skills for working with structured, semi-structured, and unstructured datasets - Utilize Docker and Kubernetes for containerization and orchestration - Experience with popular ML Ops frameworks like Kubeflow, ML Flow, and Data Robot - Ability to build ML Ops pipelines - Proficiency in Python programming - Knowledge of Kubeflow, Poetry for package management, and code clean-up tools like Black, Ruff, Isort, Flake8 - Familiarity with GitHub Actions for automation - Understanding of various machine learning techniques such as Decision Trees, Random Forest, Neural Networks, Deep Learning, etc. - Design and implement cloud solutions using Azure - Awareness of Agile/Scrum methodologies - Identify appropriate modelling approaches for different scenarios - Assess data availability and modelling feasibility - Review interpretation of model results - Experience in the logistics industry domain is advantageous Qualifications: - University Degree in Computer Science, Information Technology, or related field - 6+ years of experience in Machine Learning Operations - Certifications in core technologies - Strong analytical skills and attention to detail - Excellent communication skills for collaborating with diverse teams and stakeholders - Customer-focused mindset with a drive for continuous improvement - Ability to coach and mentor team members - Strong problem-solving skills to overcome complexities in developing and supporting data pipelines - Proficiency in spoken and written English for cross-region collaboration If you are passionate about machine learning, possess strong technical skills in Python and CI/CD pipeline, and thrive in a fast-paced, multinational environment, then this role as a Senior ML Ops Engineer at Epergne Solutions is the perfect opportunity for you. As a Senior ML Ops Engineer at Epergne Solutions, your role will involve designing data pipelines and engineering infrastructure to support enterprise machine learning systems at scale. You will be responsible for converting offline models developed by data scientists into real machine learning production systems. Your duties will include developing and deploying scalable tools and services for clients to handle machine learning training and inference efficiently. Key Responsibilities: - Design data pipelines and engineering infrastructure for enterprise machine learning systems - Transform offline models into production-ready machine learning systems - Develop and deploy scalable tools and services for machine learning training and inference - Identify and evaluate new technologies to enhance performance, maintainability, and reliability of machine learning systems - Apply software engineering best practices to machine learning, including CI/CD and automation - Support model development with a focus on auditability, versioning, and data security - Facilitate the development and deployment of proof-of-concept machine learning systems - Communicate with clients to gather requirements and track progress - Demonstrate robust analytic skills for working with structured, semi-structured, and unstructured datasets - Utilize Docker and Kubernetes for containerization and orchestration - Experience with popular ML Ops frameworks like Kubeflow, ML Flow, and Data Robot - Ability to build ML Ops pipelines - Proficiency in Python programming - Knowledge of Kubeflow, Poetry for package management, and code clean-up tools like Black, Ruff, Isort, Flake8 - Familiarity with GitHub Actions for automation - Understanding of various machine learning techniques such as Decision Trees, Random Forest, Neural Networks, Deep Learning, etc. - Design and implement cloud solutions using Azure - Awareness of Agile/Scrum methodologies - Identify