ML Ops Architect

March 5

🤠 Texas – Remote

Although this job is listed in Texas, this company may be open to hiring remote in other states.

⏰ Full Time

🟠 Senior

🔴 Lead

🤖 Machine Learning Engineer

🦅 H1B Visa Sponsor

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Tiger Analytics

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Tiger Analytics is a leading AI and analytics consulting firm that specializes in leveraging data science and machine learning to provide strategic business insights across various industries. They offer services in data strategy, AI engineering, and business intelligence to enable data-driven decision-making and digital transformation for their clients. Tiger Analytics collaborates with top technology partners like Microsoft, Google Cloud, and AWS to deliver cutting-edge solutions. They serve a diverse range of sectors including consumer packaged goods, healthcare, and finance, helping businesses operationalize insights and differentiate with AI and machine learning technologies.

Machine Learning • Predictive Analytics • Forecasting • Optimization • Natural Language Processing

1001 - 5000 employees

Founded 2011

🤖 Artificial Intelligence

🤝 B2B

📋 Description

• Tiger Analytics is an advanced analytics consulting firm. • We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. • Our consultants bring deep expertise in Data Science, Machine Learning, and AI. • Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner. • We are looking for a motivated and passionate Machine Learning Engineers for our team. • As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers that support, build, and enable Machine capabilities across the organization. • You will work closely with internal customers and infrastructure teams to build our next generation data science workbench and ML platform and products. • You will be able to further expand your knowledge and develop your expertise in modern Machine Learning frameworks, libraries and technologies while working closely with internal stakeholders to understand the evolving business needs. • If you have a penchant for creative solutions and enjoy working in a hands-on, collaborative environment, then this role is for you.

🎯 Requirements

• Implement scalable and reliable systems leveraging cloud-based architectures, technologies and platforms to handle model inference at scale. • Deploy 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. • Leverage CICD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. • Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. • Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to develop and implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, monitoring, alerting and automated model deployment. • Manage and monitor machine learning infrastructure, ensuring high availability and performance. • Implement robust monitoring and logging solutions for tracking model performance and system health. • Monitor real-time performance of deployed models, analyze performance data, and proactively identify and address performance issues to ensure optimal model performance. • Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability in a timely and efficient manner. • Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations. • Collaborate with platform engineers to effectively manage cloud compute resources for ML model deployment, monitoring, and performance optimization. • Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes, tools, and best practices.

🏖️ Benefits

Significant career development opportunities exist as the company grows. • The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.