Senior DevOps Engineer

1 day ago

Bengaluru, Karnataka, India Navikenz Full time US$ 1,25,000 - US$ 1,75,000 per year

Job Summary:

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.

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