Senior Cloud & Platform Architect

Job Title:

Senior Cloud & Platform Architect We are looking for a Senior Cloud & Platform Architect with deep experience in designing and optimizing high-performance, large-scale distributed systems. The ideal candidate will have extensive knowledge in microservices architecture, Kubernetes, and best practices for deploying and scaling Machine Learning (ML) systems. This is a critical role for our SaaS platform, which hosts multiple fine-tuned ML models and microservices, handling large transaction volumes with low latency and high throughput.

Key Responsibilities:

• Design, develop, and maintain a scalable microservices architecture that supports high throughput and low latency.
• Collaborate with ML teams to define infrastructure requirements for model training, inference, and versioning.
• Configure and optimize Kubernetes clusters for production workloads, ensuring reliable orchestration of microservices and ML components.
• Establish best practices for deployment pipelines (CI/CD), containerization (Docker), and infrastructure as code (Terraform, Helm, etc.).
• Identify bottlenecks and optimize system performance, throughput, and latency.
• Use advanced monitoring and alerting tools (e.g., Prometheus, Grafana, Datadog, Newrelic) to proactively ensure service uptime and reliability.
• Implement autoscaling policies for both microservices and ML workloads based on real-time metrics.
• Develop strategies for deploying and managing multiple ML models (model serving, versioning, A/B testing, and canary releases).
• Collaborate with ML engineers and software developers to ensure seamless integration of models into the production environment.
• Mentor junior team members in best practices for cloud, DevOps, and performance engineering.

Required Skills

• Experience: 5+ years of professional experience building and operating large-scale, high-performance distributed systems.
• Microservices: Strong understanding of microservices patterns (circuit breakers, service discovery, load balancing, etc.).
• Kubernetes Expertise: Hands-on experience with Kubernetes (installation, configuration, maintenance, production deployments).
• Cloud Platforms: Proficiency with at least one major cloud provider (AWS, GCP, Azure), including networking, security, and cost management.
• DevOps & CI/CD: Practical knowledge of CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions) and infrastructure as code (Terraform, CloudFormation).
• Performance Tuning: Proven track record in performance optimization, monitoring, logging, and alerting in high-traffic environments.
• ML Systems: Familiarity with ML frameworks (e.g., TensorFlow, PyTorch) and model serving tools (KFServing, Seldon, MLflow) is highly preferred. Experience deploying models with vLLM and performing model quantization is a plus.
• Familiarity with big data processing frameworks (e.g., Spark) and streaming/messaging systems (e.g., Kafka, RabbitMQ).
• Knowledge of observability best practices (distributed tracing, advanced logging, metrics collection).

Qualifications

• Bachelors or Master’s in Computer Science, Engineering, or related field.

If you’re passionate about building a high-performance SaaS platform with cutting-edge ML capabilities., apply now!