CoreOps.AI - DevOps Engineer - MLOps

Job Description :

Role : 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.

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

- Bachelors or Masters 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.