On-Site
Full-Time
Gurugram, Haryana
India
Skills
Problem Solving
Computer-Aided Design (CAD)
Engineering
Critical Thinking
Operational Efficiency
Skilled Multi-tasker
Finite Element Analysis (FEA)
Mechanical Engineering
Thinking Skills
Manufacturing
About the Role
Job Summary:
We are looking for a skilled MLOps Engineer who specializes in deploying and managing machine learning models using cloud-native CI/CD pipelines, FastAPI, and Kubernetes, without Docker. The ideal candidate should be well-versed in scalable model serving, API development, and infrastructure automation on the cloud using native container alternatives or pre-built images.
Key Responsibilities:
Design, develop, and maintain CI/CD pipelines for ML model training, testing, and deployment on cloud platforms (Azure/AWS/GCP).
Develop REST APIs using FastAPI for model inference and data services.
Deploy and orchestrate microservices and ML workloads on Kubernetes clusters (EKS, AKS, GKE, or on-prem K8s).
Implement model monitoring, logging, and version control without Docker-based containers.
Utilize alternatives such as Singularity, Buildah, or cloud-native container orchestration.
Automate deployment pipelines using tools like GitHub Actions, GitLab CI, Jenkins, Azure DevOps, etc.
Manage secrets, configurations, and infrastructure using Kubernetes secrets, ConfigMaps, Helm, or Kustomize.
Work closely with Data Scientists and Backend Engineers to integrate ML models with APIs and UIs.
Optimize performance, scalability, and reliability of ML services in production.
Required Skills:
Strong experience with Kubernetes (deployment, scaling, Helm/Kustomize).
Deep understanding of CI/CD tools like Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps.
Experience with FastAPI for high-performance ML/REST APIs.
Proficient in cloud platforms (AWS, GCP, or Azure) for ML pipeline orchestration.
Experience with non-Docker containerization or deployment tools (e.g., Singularity, Podman, or OCI-compliant methods).
Strong Python skills and familiarity with ML libraries and model serialization (e.g., Pickle, ONNX, TorchServe).
Good understanding of DevOps principles, GitOps, and IaC (Terraform or similar).
Preferred Qualifications:
Experience with Kubeflow, MLflow, or similar tools.
Familiarity with model monitoring tools like Prometheus, Grafana, or Seldon Core.
Understanding of security and compliance in production ML systems.
Bachelor's or Master’s degree in Computer Science, Engineering, or related field.
We are looking for a skilled MLOps Engineer who specializes in deploying and managing machine learning models using cloud-native CI/CD pipelines, FastAPI, and Kubernetes, without Docker. The ideal candidate should be well-versed in scalable model serving, API development, and infrastructure automation on the cloud using native container alternatives or pre-built images.
Key Responsibilities:
Design, develop, and maintain CI/CD pipelines for ML model training, testing, and deployment on cloud platforms (Azure/AWS/GCP).
Develop REST APIs using FastAPI for model inference and data services.
Deploy and orchestrate microservices and ML workloads on Kubernetes clusters (EKS, AKS, GKE, or on-prem K8s).
Implement model monitoring, logging, and version control without Docker-based containers.
Utilize alternatives such as Singularity, Buildah, or cloud-native container orchestration.
Automate deployment pipelines using tools like GitHub Actions, GitLab CI, Jenkins, Azure DevOps, etc.
Manage secrets, configurations, and infrastructure using Kubernetes secrets, ConfigMaps, Helm, or Kustomize.
Work closely with Data Scientists and Backend Engineers to integrate ML models with APIs and UIs.
Optimize performance, scalability, and reliability of ML services in production.
Required Skills:
Strong experience with Kubernetes (deployment, scaling, Helm/Kustomize).
Deep understanding of CI/CD tools like Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps.
Experience with FastAPI for high-performance ML/REST APIs.
Proficient in cloud platforms (AWS, GCP, or Azure) for ML pipeline orchestration.
Experience with non-Docker containerization or deployment tools (e.g., Singularity, Podman, or OCI-compliant methods).
Strong Python skills and familiarity with ML libraries and model serialization (e.g., Pickle, ONNX, TorchServe).
Good understanding of DevOps principles, GitOps, and IaC (Terraform or similar).
Preferred Qualifications:
Experience with Kubeflow, MLflow, or similar tools.
Familiarity with model monitoring tools like Prometheus, Grafana, or Seldon Core.
Understanding of security and compliance in production ML systems.
Bachelor's or Master’s degree in Computer Science, Engineering, or related field.
Apply for this position
Application Status
Application Draft
In Progress
Submit Application
Pending
Review Process
Expected within 5-7 days
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