Senior DevOps Engineer (Kubernetes & AI Infra)

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:

1. Building Scalable Infrastructure: You ll design, implement, and maintain cloud infrastructure using Kubernetes to handle AI and non-AI workloads efficiently.


2. 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.


3. Optimizing AI Model Deployments: Work with ML engineers to fine-tune LLM models, AI-driven applications, and containerized environments for smooth operation.


4. 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.


5. 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:

What We re Looking For:

1. 8+ years of experience in DevOps, SRE, or Platform Engineering role, with expertise in Kubernetes and cloud-native DevOps


2. Strong knowledge of Kubernetes fundamentals (deployments, services, ingress, storage, GPU scheduling, multi-cluster management).


3. Proficiency in scripting & automation with Python, Bash, or Go, particularly for AI-related workflows.


4. Hands-on experience with AWS, Azure, or GCP, especially in Kubernetes-based AI/ML infrastructure (e.g., Amazon SageMaker, GKE with AI, Azure ML).


5. Hands-on experience with model deployment frameworks (NVIDIA Triton, vLLM. TGI etc.)


6. Experience with Distributed computing, multi-GPU training on kubernetes and on-prem GPU clusters.


7. Experience managing resource allocation and autoscaling for large training/inference workloads. (KEDA, HPA etc.)


8. Experience with CI/CD & MLOps tools such as Jenkins, Argo CD, Kubeflow, MLflow, or Tekton.


9. Familiarity with GenAI model deployment, including fine-tuning, inference optimization, and A/B testing.


10. Hands-on experience with managed ML services (AWS bedrock, Vertext AI models etc.)


11. Strong problem-solving skills and a mindset of automating repetitive tasks.


12. Excellent communication skills to collaborate with ML engineers, data scientists, and software teams.

Bonus Points If You Have:

1. Experience with LLMOps (Large Language Model Operations) and deploying LLM-based applications at scale.


2. Knowledge of Vector Databases (FAISS, Weaviate, Qdrant) for AI-driven applications.