Senior DevOps Engineer (Kubernetes & AI Infra)

Navikenz; Bengaluru; Hybrid; INR 1,500,000 - 2,500,000; Full time

Job summary

A technology solutions company is searching for an experienced Senior DevOps Engineer in Bengaluru, India. The successful candidate will be responsible for designing and maintaining scalable cloud infrastructure while automating AI/ML workflows using Kubernetes and related tools. Candidates should have over 8 years of experience in DevOps, strong knowledge of Kubernetes fundamentals, and hands-on experience with cloud platforms like AWS or Azure. Strong problem-solving skills and scripting proficiency are essential for this role.

Qualifications

• 8+ years of experience in DevOps, SRE, or Platform Engineering roles.
• Strong knowledge of Kubernetes fundamentals, especially for AI workloads.
• Hands-on experience with AWS, Azure, or GCP in Kubernetes-based environments.

Responsibilities

• Design and maintain scalable cloud infrastructure using Kubernetes.
• Automate AI/ML workflows with tools like Kubeflow and MLflow.
• Monitor Kubernetes clusters and AI workloads for performance tuning.

Skills

Kubernetes fundamentals

Scripting with Python

AWS or Azure or GCP

CI/CD & MLOps tools

Problem-solving skills

Azure

GCP

MLOps

Tools

Kubeflow

Terraform

Prometheus

Grafana

Prometheus

Job description

We’re looking for an experienced Senior DevOps Engineer who loves working with Kubernetes and AI-driven applications. In this role, you’ll be responsible for designing, implementing, and maintaining scalable cloud infrastructure while supporting MLOps pipelines for AI workloads.

What You’ll Be Doing:

• Building Scalable Infrastructure: You’ll design, implement, and maintain cloud infrastructure using Kubernetes to handle AI and non-AI workloads efficiently.
• Developing CI/CD & MLOps Pipelines: Help us automate AI/ML workflows using tools like Kubeflow, MLflow, or Argo Workflows, ensuring seamless deployment and monitoring of AI models.
• Optimizing AI Model Deployments: Work with ML engineers to fine-tune LLM models, AI-driven applications, and containerized environments for smooth operation.
• Monitoring & Performance Tuning: Keep an eye on Kubernetes clusters and AI workloads, using tools like Prometheus, Grafana, and Loki to ensure high availability and performance.
• Automating Everything: Whether it’s infrastructure provisioning (Terraform, Helm) or Kubernetes security best practices, you’ll help enforce efficiency and compliance.

Staying Ahead of the Curve:

You’ll have the opportunity to explore and implement emerging AI infrastructure trends, including KServe, Ray, and Triton Inference Server.

What We’re Looking For:

• 8+ years of experience in DevOps, SRE, or Platform Engineering role, with expertise in Kubernetes and cloud-native DevOps
• Strong knowledge of Kubernetes fundamentals (deployments, services, ingress, storage, GPU scheduling, multi-cluster management).
• Proficiency in scripting & automation with Python, Bash, or Go, particularly for AI-related workflows.
• Hands‑on experience with AWS, Azure, or GCP, especially in Kubernetes-based AI/ML infrastructure (e.g., Amazon SageMaker, GKE with AI, Azure ML).
• Hands‑on experience with model deployment frameworks (NVIDIA Triton, vLLM. TGI etc.)
• Experience with Distributed computing, multi‑GPU training on kubernetes and on‑prem GPU clusters.
• Experience managing resource allocation and autoscaling for large training/inference workloads. (KEDA, HPA etc.)
• Experience with CI/CD & MLOps tools such as Jenkins, Argo CD, Kubeflow, MLflow, or Tekton.
• Familiarity with GenAI model deployment, including fine‑tuning, inference optimization, and A/B testing.
• Hands‑on experience with managed ML services (AWS bedrock, Vertext AI models etc.)
• Strong problem‑solving skills and a mindset of automating repetitive tasks.
• Excellent communication skills to collaborate with ML engineers, data scientists, and software teams.

Bonus Points If You Have:

• Experience with LLMOps (Large Language Model Operations) and deploying LLM‑based applications at scale.
• Knowledge of Vector Databases (FAISS, Weaviate, Qdrant) for AI‑driven applications.