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