machine learning operations

Role & responsibilities Job Description: MLOps Engineer (4 -5 Years Experience) Position MLOps Engineer Experience 45 Years Employment Type Full-Time Role Overview We are seeking an experienced MLOps Engineer to build, automate, and manage scalable machine learning infrastructure and deployment pipelines. The ideal candidate will have strong experience in CI/CD, containerization, Kubernetes, model lifecycle management, and cloud-native technologies. You will work closely with Data Scientists, ML Engineers, Software Engineers, and DevOps teams to operationalize AI/ML solutions from development to production. Key Responsibilities Design, implement, and maintain end-to-end MLOps pipelines for model training, validation, deployment, and monitoring. Build and manage CI/CD pipelines for ML applications using Jenkins, GitHub Actions, or similar tools. Deploy and manage ML workloads on Kubernetes using containerized environments. Implement and maintain MLflow for experiment tracking, model registry, and model lifecycle management. Develop Infrastructure as Code (IaC) using Terraform or Ansible. Automate model deployment using Docker, Kubernetes, and GitOps practices. Manage feature, model, and artifact versioning. Optimize GPU/CPU resource utilization for training and inference workloads. Implement monitoring, logging, and alerting for ML systems. Collaborate with data scientists to productionize machine learning models. Ensure reproducibility, scalability, security, and governance of ML workflows. Troubleshoot production ML systems and optimize deployment performance. Required Skills MLOps & ML Platforms MLflow (Experiment Tracking, Model Registry) Kubeflow (preferred) DVC (Data Version Control) Feature Store concepts (Feast is a plus) CI/CD & DevOps Jenkins GitHub Actions GitLab CI/CD (good to have) ArgoCD / FluxCD (GitOps) SonarQube Nexus/Artifactory Containerization & Orchestration Docker Kubernetes Helm Charts Kustomize Cloud Platforms Experience in one or more: AWS (mandatory) Azure Google Cloud Platform (GCP) Hands-on with services such as: EKS / AKS / GKE S3 / Blob Storage / Cloud Storage IAM Container Registry Secrets Management Infrastructure Automation Terraform Ansible CloudFormation (good to have) Programming Python (Strong) Bash/Shell scripting SQL ML Frameworks Experience working with one or more: PyTorch TensorFlow Scikit-learn Hugging Face Transformers Monitoring & Observability Prometheus Grafana ELK Stack / OpenSearch Loki CloudWatch (AWS) Version Control Git GitHub Git branching strategies Good to Have Experience with Airflow or similar workflow orchestration tools. Experience with Ray, KServe, Seldon Core, or BentoML. Knowledge of LLMOps, RAG pipelines, vector databases (Pinecone, Milvus, Weaviate, OpenSearch), and inference servers such as vLLM or NVIDIA Triton. Experience deploying models on GPU infrastructure. Familiarity with ML security, governance, and Responsible AI practices. Experience working in Linux production environments. Qualifications Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, or a related field. 4–5 years of experience in DevOps, MLOps, Platform Engineering, or Machine Learning Infrastructure. Proven experience deploying and maintaining production-grade ML systems. Strong understanding of software engineering best practices, CI/CD, and cloud native architectures. Preferred Certifications AWS Certified Solutions Architect / DevOps Engineer Certified Kubernetes Administrator (CKA) or Certified Kubernetes Application Developer (CKAD) HashiCorp Terraform Associate Docker Certified Associate What We're Looking For Strong problem-solving and troubleshooting skills. Ability to work in cross-functional AI engineering teams. Passion for automation, scalability, and platform engineering. Experience building reliable, secure, and production-ready ML platforms. Excellent communication and documentation skills.