Architect - AI and Machine Learning Platforms

Overview

Join as an Architect in a hybrid work environment to lead the design and enhancement of AI and machine learning platforms on Google Cloud. This role involves leveraging feature stores, MLFlow, ML Ops practices, Docker, and Kubernetes to build scalable enterprise solutions. The focus is on delivering robust AI-driven applications and pipelines that support telecom, billing, and revenue management domains.

Key Responsibilities

• Develop scalable AI/ML platform architectures integrating feature store capabilities, model training processes, and prediction services tailored for complex enterprise scenarios.
• Implement and refine ML Ops frameworks for standardized experiment tracking, model version control, and deployment using MLFlow, Google Vertex AI, and Git repositories.
• Set up and maintain Continuous Integration and Continuous Delivery (CI/CD) pipelines with Jenkins to automate testing and release of machine learning applications, emphasizing reliability and quick iteration.
• Orchestrate data and model workflows via Airflow, managing dependencies, scheduling, and monitoring across the data lifecycle stages.
• Design containerized solutions using Docker and Kubernetes to ensure portability, resilience, and scalability across Google Cloud environments.
• Apply advanced AI/ML theories to select suitable algorithms, engineer features, and define evaluation metrics consistent with business goals and ethical standards.
• Collaborate closely with cross-functional teams including data scientists and engineers to translate telecom and billing domain requirements into effective technical solutions.
• Establish governance strategies for machine learning models encompassing monitoring, drift detection, audits, and documentation to maintain compliance and operational integrity.
• Employ Terraform for infrastructure as code to provision and manage cloud resources securely and consistently.
• Analyze pipeline performance, identify bottlenecks, and recommend architectural optimizations to boost efficiency, scalability, and cost-effectiveness.
• Facilitate collaboration in hybrid working settings through clear documentation, standardized approaches, and tool usage, supporting effective AI solution delivery during day shift hours.
• Ensure data security, privacy, and responsible usage in all AI/ML designs by adhering to company policies and industry standards.
• Communicate complex architecture decisions and their strategic impact to stakeholders with clarity and precision.

Qualifications

• Proven expertise in feature store implementations and ML Ops practices including experiment tracking, model lifecycle management, and automated deployments.
• Strong knowledge of Jenkins, Airflow, and Git for CI/CD integration, complex pipeline orchestration, and version control management.
• Advanced skills in containerization technologies like Docker and Kubernetes, and infrastructure automation using Terraform, particularly within Google Cloud ML services.
• In-depth understanding of AI/ML theories and modeling techniques to deliver reliable and measurable business outcomes.
• Experience or understanding of telecom, billing, and revenue management domains, especially in rating and charging processes.
• Excellent analytical problem-solving ability, strong communication skills, and experience working collaboratively across diverse teams in a hybrid environment.

Eligibility

Applicants must hold a graduate degree to qualify for this position.