ML Architect

The Lead ML Architect will own the end-to-end design, development, and deployment of advanced ML and AI platforms, driving the architecture and technical direction for high-performance, scalable machine learning solutions. The role demands extensive hands-on expertise in building machine learning platforms, leveraging AWS SageMaker, Python-based frameworks, and parallel computing for efficient large-scale data processing.

Key Responsibilities:

• Lead the architecture, design, and implementation of robust, scalable, and high-performing ML and AI platforms.
• Design and develop end-to-end ML workflows and pipelines using AWS SageMaker, Python, and distributed computing technologies.
• Hands-on implementation of parallel computing and distributed training methodologies to enhance the efficiency and scalability of machine learning models.
• Collaborate closely with data scientists and engineers to deploy complex ML and deep learning models into mission-critical production systems.
• Ensure best practices in CI/CD, containerization, orchestration, and infrastructure-as-code are consistently applied across platforms.
• Foster a culture of innovation, continuous improvement, and self-service analytics across the team and organization.
• Lead and mentor a high-performing team of ML engineers, promoting best practices, technical excellence, and continuous learning.
• Ensure compliance with model governance standards, data security best practices, and regulatory requirements (e.g., GDPR, HIPAA).
• Implement robust monitoring, alerting, and logging strategies to ensure consistent performance and rapid incident response for ML systems in production.
• Continuously optimize the performance, scalability, and cost-effectiveness of deployed ML models.
• Partner closely with product management and executive stakeholders to ensure alignment of ML platform development with strategic business objectives.
• Stay abreast of latest advancements in ML and AI technologies, proactively applying new techniques and tools to deliver superior outcomes.

Day-to-Day Activities:

• Design, build, and maintain scalable ML platform infrastructure and tooling.
• Develop optimized machine learning models, leveraging advanced supervised and unsupervised techniques.
• Rapidly prototype and iterate on proof-of-concepts and transition successful prototypes into enterprise-grade solutions.
• Conduct comprehensive reviews and present findings clearly to technical and non-technical stakeholders.
• Continuously perform horizon scanning and research emerging ML trends, tools, and methodologies.

Knowledge and Skills:

Essential:

• Deep expertise in designing and deploying ML/AI platforms, specifically using AWS SageMaker.
• Strong proficiency in Python and its ecosystem (e.g., TensorFlow, PyTorch, scikit-learn).
• Extensive hands-on experience with parallel computing frameworks and distributed processing.
• Proficient in SQL, ETL, data warehousing, and data modeling techniques.
• Thorough understanding of statistical analysis, predictive modeling, and data mining methodologies.
• Proven capability of deploying machine learning models into production at scale.
• Familiarity with CI/CD pipelines, containerization (Docker), and version control (Git).

Preferred:

• Experience with Kubernetes orchestration systems.
• Familiarity with Snowflake data warehousing and NoSQL databases like MongoDB.
• Hands-on experience with messaging systems (e.g., Kafka).
• Exposure to distributed computing frameworks such as Hadoop, Spark, and Ray.