Job Title: MLOps Engineer
Company: Aaizel International Technologies Pvt. Ltd.
Location: On Site
Experience Required: 8+ Years
Employment Type: Full-Time (Immediate Joiner 2-3 days)
About Aaizeltech
Aaizeltech is a deep-tech company building AI/ML-powered platforms, scalable SaaS applications, and intelligent embedded systems. We are seeking a Senior MLOps Engineer to lead the architecture, deployment, automation, and scaling of infrastructure and ML systems across multiple product lines.
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.
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
