AI DevOps / Cloud Engineer (MLOps)

Company Overview

Role Overvie

Responsibiliti

• Design, implement, and manage cloud infrastructure on AWS, including networking, compute, storage, security, and monitoring service
• Build and maintain Infrastructure as Code (IaC) frameworks using Terraform and CloudFormatio
• Develop and manage CI/CD pipelines for machine learning, data engineering, and software engineering teams using GitHub Actions or GitLab C
• Establish GitOps workflows, deployment standards, automated testing frameworks, and release management processe
• Own Docker and Kubernetes (EKS) environments, ensuring scalability, performance, reliability, and cost optimizatio
• Collaborate closely with Data Scientists and ML Engineers to deploy, manage, and scale machine learning models in production environment
• Build and maintain model serving infrastructure, batch processing pipelines, and real-time inference platform
• Implement workflow orchestration solutions using Airflow, Dagster, or Prefect for data and machine learning pipeline
• Develop comprehensive monitoring, observability, logging, and alerting frameworks across infrastructure, applications, and ML system
• Establish model monitoring capabilities including drift detection, data quality monitoring, experiment tracking, and automated retraining workflow
• Manage AWS security controls, IAM policies, Secrets Manager, KMS encryption, network security, and access governanc
• Support integration of customer data platforms, analytics platforms, mobile attribution systems, and engagement platforms into the central cloud ecosyste
• Maintain operational documentation, infrastructure standards, disaster recovery procedures, and engineering runbook
• Support general technology operations for the AI team, including environment provisioning, access management, tooling administration, and incident response activitie
• Drive continuous improvements in reliability, scalability, automation, and operational efficiency across the AI platform.

Requirements

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related fie
• 5–10+ years of experience in DevOps Engineering, Cloud Engineering, Site Reliability Engineering, or Infrastructure Engineering role
• Strong hands-on expertise with AWS services including EC2, EKS, S3, RDS, Lambda, IAM, VPC, CloudWatch, and SageMaker
• Proven experience building cloud environments and infrastructure platforms from scratch
• Strong experience with Kubernetes, Docker, Helm, and containerized production environments
• Hands-on experience with Infrastructure as Code tools such as Terraform and CloudFormation
• Experience developing and managing CI/CD pipelines using GitHub Actions, GitLab CI, or similar technologies.
• Exposure to machine learning infrastructure, MLOps practices, and model deployment workflow
• Experience with workflow orchestration platforms such as Airflow, Dagster, or Prefect
• Knowledge of ML lifecycle management tools including MLflow, Weights & Biases, and model monitoring frameworks
• Experience implementing observability solutions using Datadog, Grafana, Prometheus, CloudWatch, ELK, or similar platforms
• Strong understanding of cloud security, access management, encryption, secrets management, and compliance requirements
• Familiarity with modern data platforms including Databricks, Spark, Delta Lake, Apache Iceberg, and cloud-based lakehouse architecture
• Experience integrating platforms such as Segment, Amplitude, Firebase, Adjust, MoEngage, or similar data and engagement tools
• Strong troubleshooting, problem-solving, and stakeholder communication skills
• Previous experience working within AI, data, fintech, technology, or high-growth product environments is highly preferred
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Preferred Qualifications

• Experience supporting enterprise AI or machine learning platforms at scale.
• Knowledge of Kafka, RabbitMQ, or event-driven architectures.
• Experience with Azure cloud services.
• Background in financial services, fintech, trading, or data-intensive technology organizations.
• AWS Professional, Kubernetes, Terraform, or related cloud certifications.