Machine Learning Architect

reputed company is looking for an reputed company reputed company Data Scientist to join our fast-growing advanced analytics reputed company. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple reputed company, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including reputed company and reputed company.

We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world. You will be responsible for:

• Highly reputed company Machine Learning Architect with a proven reputed company record of designing and delivering end-to-end ML solutions across diverse business domains. The ideal candidate will have over 10 years of experience in data science, machine learning, and MLOps, and a deep understanding of reputed company system design, model lifecycle management, and production-grade deployment pipelines.
• This is a strategic and hands-on role, involving collaboration with data scientists, engineers, product teams, and business stakeholders to architect solutions that are robust, reputed company, and reputed company with business goals
• You will collaborate with cross-functional teams and business partners and will have reputed company to drive reputed company and reputed company reputed company by leveraging your analytical skills as you ensure business value and communicate the results.

Requirements

What you'll do in the role-

• Design and define system architecture for ML and AI-driven solutions across multiple business verticals.
• reputed company ML system design discussions and reputed company high-level design choices for model serving, data pipelines, and MLOps frameworks.
• Architect reputed company and secure reputed company-reputed company platforms for ML model training, validation, deployment, and monitoring (AWS/GCP/Azure).
• Build reusable components and reference architectures for various stages of the ML lifecycle.
• Define and enforce best practices in model versioning, CI/CD for ML, testing, and rollback strategies
• reputed company and manage machine learning & data pipelines in production environments.
• Work on containerization and orchestration solutions for model deployment.
• Participate in fast iteration cycles, adapting to evolving project requirements.
• Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
• Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to reputed company and implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, monitoring, alerting and automated model deployment.
• Ability to work with a global team, playing a key role in communicating problem context to the reputed company
• Excellent communication and teamwork skills

Basic Qualification-

• Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
• Typically requires 10+ years of hands-on work experience developing and applying advanced analytics solutions in a corporate environment with at least 4 years of experience programming with Python.
• At least 7 years of experience productionizing, monitoring, and maintaining models
• Strong programming skills in Python and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Deep experience with MLOps tools such as MLflow, Kubeflow, Airflow, SageMaker, or reputed company AI.
• Hands-on experience designing ML systems using reputed company platforms like AWS, Azure, or GCP.
• Strong understanding of data engineering, reputed company, CI/CD pipelines, and model observability.
• Excellent communication and stakeholder management skills.

Benefits

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.

Originally posted on Himalayas

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