Data Science Architect

Sapphirus Systems; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Sapphirus Systems in Bangalore seeks an experienced Data Science Architect to design scalable, secure data science architectures for analytics and AI-based decision-making. You will lead end-to-end solutions from data acquisition to deployment and monitoring, ensuring governance and Responsible AI compliance.

The role requires 14–17 years of experience in AI/ML, with strong Python/R/SQL skills, and expertise across cloud platforms (AWS/Azure/GCP), NLP, CV, and time-series forecasting.

Qualifications

• Experience designing scalable data science architectures for analytics and AI-based decisions.
• Proven ability to lead end-to-end ML solutions from data to deployment and monitoring.
• Strong knowledge of ML lifecycle, governance, and Responsible AI principles.

Responsibilities

• Design scalable, secure, and high-performance data science and machine learning architectures to support advanced analytics and AI-driven decision-making.
• Lead the architecture and implementation of end-to-end analytical solutions, from data acquisition and feature engineering to model deployment and monitoring.
• Establish standards, frameworks, and best practices for data science, machine learning, MLOps, model governance, and AI solution development.
• Collaborate with business, product, technology, and analytics stakeholders to identify opportunities and translate business challenges into data science solutions.
• Define architectures for predictive analytics, forecasting, optimization, recommendation systems, NLP, computer vision, and other advanced analytical applications.
• Oversee the development and deployment of machine learning models, ensuring scalability, reliability, explainability, and operational excellence.
• Establish model lifecycle management processes, including experimentation, versioning, validation, deployment, monitoring, and continuous improvement.
• Ensure adherence to Responsible AI principles, including fairness, transparency, explainability, privacy, security, and regulatory compliance.
• Partner with Data Engineering and Enterprise Architecture teams to design integrated data ecosystems that support AI and advanced analytics workloads.
• Evaluate and recommend emerging technologies, tools, and industry best practices to enhance organizational AI and data science capabilities.
• Mentor and provide technical leadership to Data Scientists, ML Engineers, and Analytics teams while fostering innovation and knowledge sharing.
• Strong expertise in Statistics, Machine Learning, Predictive Modeling, and Advanced Analytics.Proficiency in Python, R, SQL, and leading data science libraries and frameworks.
• Experience with Machine Learning, Deep Learning, NLP, Time Series Forecasting, and Optimization techniques.
• Strong understanding of MLOps, CI/CD, model deployment, monitoring, and model governance.Experience with cloud platforms such as Azure, AWS, or GCP.
• Knowledge of distributed computing and big data technologies such as Spark, Hadoop, or equivalent platforms.
• Expertise in data visualization, storytelling, and communicating complex analytical concepts to business stakeholders.
• Strong understanding of data governance, data quality, security, privacy, and Responsible AI principles. Excellent leadership, stakeholder management, problem-solving, and consulting skills.
• Experience architecting and delivering enterprise-scale AI, analytics, and data science solutions across multiple business domains.

Skills

AI

ML

Computer vision

Deep Learning

AWS

Azure

GCP

LLM

MLOps

Python

R

SQL

Statistics

CI/CD

Spark

Job description

Job Title:Data Science Architect

Experience: 14 to 17 Years

Location: Bangalore

we are looking Data scientist with machine learning and any cloud experience

Key Skills: AI , ML , Computer vision , Deep Learning , AWS, Azure , GCP, along with this LLM, MLOPS is required

Domain Expertise: Utilities & Energy Sector (Power, Electricity, Renewable Energy, Oil & Gas, Water Utilities)

Job description:

• Design scalable, secure, and high-performance data science and machine learning architectures to support advanced analytics and AI-driven decision-making.
• Lead the architecture and implementation of end-to-end analytical solutions, from data acquisition and feature engineering to model deployment and monitoring.
• Establish standards, frameworks, and best practices for data science, machine learning, MLOps, model governance, and AI solution development.
• Collaborate with business, product, technology, and analytics stakeholders to identify opportunities and translate business challenges into data science solutions.
• Define architectures for predictive analytics, forecasting, optimization, recommendation systems, NLP, computer vision, and other advanced analytical applications.
• Oversee the development and deployment of machine learning models, ensuring scalability, reliability, explainability, and operational excellence.
• Establish model lifecycle management processes, including experimentation, versioning, validation, deployment, monitoring, and continuous improvement.
• Ensure adherence to Responsible AI principles, including fairness, transparency, explainability, privacy, security, and regulatory compliance.
• Partner with Data Engineering and Enterprise Architecture teams to design integrated data ecosystems that support AI and advanced analytics workloads.
• Evaluate and recommend emerging technologies, tools, and industry best practices to enhance organizational AI and data science capabilities.
• Mentor and provide technical leadership to Data Scientists, ML Engineers, and Analytics teams while fostering innovation and knowledge sharing.
• Strong expertise in Statistics, Machine Learning, Predictive Modeling, and Advanced Analytics.Proficiency in Python, R, SQL, and leading data science libraries and frameworks.
• Experience with Machine Learning, Deep Learning, NLP, Time Series Forecasting, and Optimization techniques.
• Strong understanding of MLOps, CI/CD, model deployment, monitoring, and model governance.Experience with cloud platforms such as Azure, AWS, or GCP.
• Knowledge of distributed computing and big data technologies such as Spark, Hadoop, or equivalent platforms.
• Expertise in data visualization, storytelling, and communicating complex analytical concepts to business stakeholders.
• Strong understanding of data governance, data quality, security, privacy, and Responsible AI principles. Excellent leadership, stakeholder management, problem-solving, and consulting skills.
• Experience architecting and delivering enterprise-scale AI, analytics, and data science solutions across multiple business domains.