[Hiring] Machine Learning Architect @System Soft Technologies

Role Description

We are seeking an experienced Machine Learning Architect to lead the design, development, and deployment of enterprise-scale AI and Machine Learning solutions. The ideal candidate will define ML architecture, guide data science and engineering teams, establish MLOps best practices, and deliver scalable AI-driven products that solve complex business problems. The Machine Learning Architect will work closely with business stakeholders, data scientists, software engineers, cloud architects, and product teams to transform business requirements into production-ready AI solutions.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, Statistics, Engineering, or related field.
• 10+ years of overall software engineering experience.
• 5+ years of experience designing enterprise Machine Learning solutions.
• Strong expertise in Machine Learning, Deep Learning, and AI architecture.
• Hands-on experience with Python and ML frameworks.
• Experience deploying ML solutions in production environments.
• Strong understanding of software architecture patterns and distributed systems.
• Experience with cloud-native AI services.

Requirements

• Define enterprise AI/ML architecture, standards, frameworks, and best practices.
• Design scalable machine learning systems from data ingestion through model deployment and monitoring.
• Evaluate and recommend appropriate AI/ML technologies, tools, and frameworks.
• Develop AI roadmaps aligned with business objectives and digital transformation initiatives.
• Lead architecture reviews and provide technical governance for AI projects.
• Architect solutions involving supervised, unsupervised, reinforcement learning, and deep learning models.
• Guide data scientists in feature engineering, model selection, training, validation, and optimization.
• Design ML pipelines for automated training, testing, deployment, and retraining.
• Ensure explainability, fairness, and ethical AI practices.
• Design and implement MLOps frameworks using Azure, AWS, or Google Cloud.
• Build CI/CD pipelines for ML model deployment and lifecycle management.
• Architect containerized ML solutions using Docker and Kubernetes.
• Implement model monitoring, observability, drift detection, and automated retraining workflows.
• Ensure high availability, security, and scalability of AI platforms.
• Collaborate with data engineering teams to establish data pipelines and feature stores.
• Design architectures supporting structured, semi-structured, and unstructured data.
• Develop APIs and microservices for model serving and enterprise integrations.
• Work with large-scale data processing frameworks.
• Mentor Data Scientists, ML Engineers, and AI Developers.
• Collaborate with business leaders to identify AI opportunities and prioritize initiatives.
• Conduct technical workshops and architecture discussions.
• Drive innovation through adoption of emerging AI technologies and industry best practices.

Company Description