DevOps / ML-Ops Engineer

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: https://coreops.ai

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.