DevOps / ML-Ops Engineer

CoreOps.AI

4 - 9 years

5 - 9 Lacs

noida

Posted:-1 days ago| Platform:

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Skills Required

automation compliance linux networking mysql shell scripting workflow apache sql python

Work Mode

Work from Office

Job Type

Full Time

Job Description

Design, build, and maintain CI/CD pipelines for applications and AI/ML workloads.
Implement Infrastructure as Code (Terraform, Ansible, CloudFormation).
Deploy and manage containerized environments using Docker, Kubernetes, and Helm.
Manage and optimize cloud infrastructure across AWS, Azure, or GCP.
Ensure system reliability, security, and performance with strong Linux administration skills.
Manage web servers, DNS, CDN, and databases (SQL/NoSQL).
Implement monitoring, logging, and alerting using Prometheus, Grafana, ELK, or Datadog.
Apply best practices for networking (VPCs, load balancers, DNS, firewalls, service meshes), scaling, and cost optimization.
MLOps
Deploy, monitor, and maintain ML models in production.
Build automated training, testing, retraining, and data drift detection pipelines.
Support data pipelines, versioning, and reproducibility (DVC, MLflow, CML).
Collaborate with data scientists and ML engineers to productionize ML models.
Integrate ML/AI workflows into CI/CD processes.
Work with APIs (REST/gRPC) for model/service integration.
Security & Compliance
Design secure, compliant systems (IAM, RBAC, secrets management, audit readiness).
Implement DevSecOps practices in CI/CD pipelines.
Ensure alignment with industry standards (GDPR, SOC2, ISO27001).
Must-Have Skills
Linux expertise: Strong hands-on Linux administration (Ubuntu, RHEL, CentOS).
DevOps foundation: CI/CD, Kubernetes, Docker, Terraform/Ansible, monitoring, and security.
Cloud experience: Hands-on with AWS, Azure, or GCP.
Networking expertise: Strong knowledge of VPCs, load balancers, DNS, firewalls, and service meshes.
Web infrastructure: Experience with Nginx, Apache, DNS management, CDN integration.
Database experience: SQL (MySQL, PostgreSQL) and NoSQL (MongoDB, Redis).
Programming: Proficiency in Python, Bash/Shell scripting, and SQL.
MLOps knowledge: Model deployment, pipelines, monitoring, retraining, and data drift detection.
Version control & automation: GitHub/GitLab, Jenkins, GitHub Actions.
ML frameworks: Familiarity with TensorFlow, PyTorch, or Scikit-learn.
RAG & AI pipeline exposure: RAG pipelines, vector databases (Pinecone, Weaviate, FAISS), embeddings, LangChain/LlamaIndex.
Collaboration tools: Jira, Azure DevOps, Confluence.
Preferred Skills
Observability and monitoring for ML/AI systems.
Familiarity with cloud-native ML platforms (SageMaker, Vertex AI, Azure ML).
Experience with workflow/data orchestration (Airflow, Argo, Kubeflow).
Security practices in DevOps/MLOps (secrets management, IAM, RBAC, compliance).
Knowledge of LLMOps best practices (monitoring, evaluation, guardrails).
Certifications (optional but attractive):
AWS/Azure/GCP Certified Solutions Architect or DevOps Engineer.
Kubernetes (CKA/CKAD/CKS).
Qualifications
Bachelor s or Master s degree in Computer Science, Engineering, or related field.
4+ years of experience in DevOps (cloud, containers, automation, Linux, networking) and 2+ years of MLOps exposure in production environments.
Strong understanding of DevOps and MLOps principles and best practices.

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