Role Overview

This role requires strong expertise and hands-on MLOps experience. You will architect and manage cloud infrastructure, CI/CD systems, Kubernetes clusters, and full ML pipelines—from data ingestion to deployment and drift monitoring.

Key Responsibilities

MLOps Responsibilities:

• Collaborate with data scientists to operationalize ML workflows.
• Build complete ML pipelines with Airflow , Kubeflow Pipelines , or Metaflow .
• Deploy models using KServe , Seldon Core , BentoML , TorchServe , or TF Serving .
• Package models into Docker containers using Flask or FastAPI or Django for APIs.
• Automated dataset versioning & model tracking via DVC and MLflow .
• Setup model registries and ensure reproducibility and audit trails .
• Implement model monitoring for:

(i)  Data drift and schema validation (using tools like Evidently AI , Alibi Detect ).

(ii) Performance metrics (accuracy, precision, recall).

(iii) Infrastructure metrics (latency, throughput, memory usage).

• Implement event-driven retraining workflows triggered by drift alerts or data freshness.
• Schedule GPU workloads on Kubernetes and manage resource utilization for ML jobs.
• Design and manage secure, scalable infrastructure using AWS , GCP , or Azure .
• Build and maintain CI/CD pipelines using Jenkins , GitLab CI , GitHub Actions , or AWS DevOps .
• Write and manage Infrastructure as Code using Terraform , Pulumi , or CloudFormation .
• Automated configuration management with Ansible , Chef , or SaltStack .
• Manage Docker containers and advanced Kubernetes resources (Helm, StatefulSets, CRDs, DaemonSets).
• Implement robust monitoring and alerting stacks: Prometheus , Grafana , CloudWatch , Datadog , ELK , or Loki .

Must-Have Skills

• Advanced expertise in Linux administration , networking, and shell scripting.
• Strong knowledge of Docker , Kubernetes , and container security.
• Hands-on experience with IaC tools like Terraform and configuration management like Ansible.
• Proficient in cloud-native services : IAM, EC2, EKS/GKE/AKS, S3, VPCs, Load Balancing, Secrets Manager.
• Mastery of CI/CD tools (e.g., Jenkins, GitLab, GitHub Actions).
• Familiarity with SaaS architecture , distributed systems, and multi-env deployments.
• Proficiency in Python for scripting and ML-related deployments.
• Experience integrating monitoring , alerting , and incident management workflows.
• Strong understanding of DevSecOps , security scans (e.g., Trivy , SonarQube , Snyk ) and secrets management

tools ( Vault , SOPS ).

• Experience with GPU orchestration and hybrid on-prem + cloud environments.

Nice-to-Have Skills

• Knowledge of GitOps workflows (e.g., ArgoCD, FluxCD).
• Experience with Vertex AI , SageMaker Pipelines , or Triton Inference Server .
• Familiarity with Knative , Cloud Run , or serverless ML deployments .
• Exposure to cost estimation , rightsizing , and usage-based autoscaling .
• Understanding of ISO 27001 , SOC2 , or GDPR-compliant ML deployments .
• Knowledge of RBAC for Kubernetes and ML pipelines .

Who You'll Work With

• AI/ML Engineers, Backend Developers, Frontend Developers, QA Team
• Product Owners, Project Managers, and external Government or Enterprise Clients

How to Apply

If you are passionate about embedded systems and excited to work on next-generation technologies, we would love to hear from you. Please send your resume and a cover letter outlining your relevant experience to hr@aaizeltech.com or anju@aaizeltech.com