Smarsh is hiring a
Machine Learning Principal Architect

Summary

Join Smarsh as a Machine Learning Principal Architect to lead the design, development, and deployment of innovative machine learning solutions across our organization. This role involves driving strategic ML initiatives, shaping the overall architecture, and ensuring that the machine learning models and systems are scalable, reliable, and optimized for performance.

Requirements

• Master’s or Ph.D. in Computer Science, Data Science, Machine Learning, or a related field
• Experience in machine learning, data science, or related fields
• Proven experience in leading the development and deployment of large-scale ML systems
• Strong background in statistical modeling, data mining, and machine learning algorithms

Responsibilities

• Lead the design and development of scalable, high-performance machine learning architectures that align with business goals
• Define and implement best practices for ML model development, deployment, and maintenance
• Ensure that ML systems are robust, scalable, and secure
• Drive the strategic vision for machine learning within the organization
• Collaborate with stakeholders to understand business needs and translate them into ML solutions
• Oversee the end-to-end process of testing, deploying, and monitoring ML models
• Work closely with data scientists, data engineers, and software engineers to integrate ML models into production systems
• Ensure models are optimized for performance, scalability, and cost-efficiency
• Lead and mentor a team of machine learning engineers and data scientists
• Foster a culture of continuous learning and innovation within the ML team
• Collaborate with cross-functional teams, including product managers, data engineers, and business analysts, to deliver ML solutions that drive business value
• Communicate technical concepts and the value of ML solutions to non-technical stakeholders

Preferred Qualifications

• Experience with deep learning, natural language processing (NLP), GenAI
• Familiarity with MLOps practices and tools
• Contributions to open-source ML projects or publications in reputable journals

Benefits

• Competitive salary along with company bonus
• Strong maternity and paternity scheme
• A workplace pension scheme
• Take what you need holiday package
• Private medical insurance
• Dental plan
• Group life assurance
• Group income protection
• Employee assistance programme
• A monthly wellness allowance
• Adoption assistance
• Stock options