As a Senior DevOps/ML Infrastructure Engineer, you'll manage infrastructure, support ML model integration, and build automated MLOps pipelines in a collaborative setting.
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As a Senior DevOps / ML Infrastructure Engineer in our AI Lab, you'll maintain and scale our infrastructure while enabling seamless ML model integration into production workflows.
You'll work alongside our Senior MLOps Architect to build a comprehensive ML platform that serves multiple teams across the organization.
What You'll Do:
- Manage multiple orchestration platforms: Kubernetes in AWS (CloudFormation) and on-prem Kubernetes clusters-
- Maintain Apache Flink infrastructure (managed in AWS or self-hosted in on-prem Kubernetes)
- Handle production support, incident response, and on-call rotations
- Perform regular patching activities and security vulnerability remediation
- Support and maintain workflow engine infrastructure
- Improve observability by utilizing Prometheus, Grafana, Splunk, Slack alerts, etc.
MLOps & Platform Development:
- Collaborate with Senior MLOps Architect to build and maintain ML infrastructure
- Set up and configure MLflow for experiment tracking and model registry
- Build automated MLOps pipelines for model training, experimentation, and deployment (Champion-Challenger, shadow mode)
- Support feature calculation pipelines and ETL processes
- Enable model serving infrastructure for Python-based ML services
We're Looking For:
- 3-5+ years of professional experience in DevOps or infrastructure engineering
- Strong hands-on experience with AWS services (EKS, ECR, SQS, S3, Managed Kafka, Managed Prometheus)
- Deep experience with Kubernetes in production environments (multi-cluster management is a plus)
- Proficiency with infrastructure as code: AWS CloudFormation and CDK (AWS Cloud Development Kit)
- Experience with containerization (Docker) and container orchestration
- Knowledge of setting up and maintaining CI/CD pipelines (GitHub Actions, ArgoCD, Jenkins, etc.)
- Hands-on experience with observability tools: Prometheus, Grafana, Splunk- Experience with production support, incident response, and on-call rotations
- Strong communication skills (English B2+)
- Ability to work collaboratively with cross-functional teams (MLOps engineers, data scientists, software engineers)
It would be a plus:
- Experience with Apache Flink, Kafka, or other stream processing frameworks
- Understanding of ML lifecycle: model training, evaluation, deployment patterns
- Experience with workflow engines or rule engines
- Knowledge of fraud prevention, fintech, or compliance domains
- Understanding of feature stores, ETL pipelines, and data engineering concepts
What We Offer:
- Remote work flexibility – work from anywhere- B2B contract with competitive gross compensation in USD
- Top-tier hardware to support your productivity
- A challenging role in a team of skilled professionals with opportunity to grow into MLOps specialization
- Direct collaboration with Senior MLOps Architect to learn and contribute to ML platform development
- Continuous learning and career growth opportunities
- Coverage for professional development: training, seminars, and conferences
- Access to high-quality English lessons
- Impact: Your work will directly prevent fraud while enabling secure financial access globally
Why This Role:
This position offers a unique opportunity to work at the intersection of traditional DevOps and MLOps. You'll maintain critical infrastructure while building expertise in ML infrastructure, model deployment, and workflow integration. You'll complement our MLOps Architect by handling general infrastructure needs while growing your ML platform skills, ultimately enabling faster delivery of ML capabilities across the organization.
Top Skills
Apache Flink
Argocd
AWS
Docker
Github Actions
Grafana
Jenkins
Kubernetes
Mlflow
Prometheus
Splunk
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