DevOps / ML-Ops Engineer

2 days ago

Noida, Uttar Pradesh, India CoreOps Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Overview
We are seeking a highly skilled DevOps Engineer with strong MLOps expertise to join our team. The ideal candidate will have a solid foundation in DevOps practices—infrastructure automation, CI/CD, container orchestration, networking, monitoring, Linux system administration, and security compliance—and extend this expertise into operationalizing ML/AI workloads. You will collaborate with data scientists, ML engineers, and software teams to ensure reliable, secure, and scalable deployments of applications and ML models.

Website:

Key Responsibilities

DevOps

  • 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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