Requirements & Qualifications

● Experience: 8+ years of professional engineering experience, with at least 4+ years

dedicated to MLOps, ML Platform Infrastructure, or Cloud Engineering at scale.

● Education: Bachelor’s or Master’s degree in Computer Science, Software Engineering,

Information Technology, or a related field.

● Core Technical Expertise:

○ MLOps Frameworks: Hands-on mastery with platforms like MLflow, Kubeflow,

Airflow, Weights & Biases, Argo Workflows, or SageMaker.

○ Containerization & Orchestration: Advanced expertise in Docker, Kubernetes

(EKS/GKE/AKS), Helm charts, and ingress controllers.

○ CI/CD & IaC: Strong command of GitHub Actions, GitLab CI, Jenkins, and

Infrastructure-as-Code tools like Terraform.

○ Monitoring & Observability: Proficiency with Prometheus, Grafana, ELK Stack,

Datadog, or specialized ML monitoring tools (Evidently AI, Whylogs, Arize).

○ Programming & Scripting: Expert proficiency in Python, Bash, and SQL for

automation, CLI tooling, and service integration.

● Location & Work Mode: Willingness to work 5 days from office at either our Gurugram or

Chennai location.

Preferred Qualifications

● Experience with LLMOps (deploying, serving, and monitoring Large Language Models

using vLLM, Ollama, or Triton Inference Server).

● Hands-on experience managing GPU compute clusters, CUDA acceleration, and

distributed inference/training.

● Relevant certifications in AWS/GCP/Azure Cloud Architecture or Kubernetes

(CKA/CKAD).