ML Operation Engineer

Job Title: MLOps Engineer

Company: Aaizel International Technologies Pvt. Ltd.

 Location: On Site

 Experience Required: 3-6 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.

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