ROLE AND RESPONSIBILITIES

- 5–8 years of experience in software engineering, platform engineering,

- DevOps, SRE, MLOps, or related infrastructure roles.

- Strong hands-on experience with Kubernetes, including writing Kubernetes Operators and Custom Resource Definitions using frameworks such as Kubebuilder, Operator SDK, or equivalent.

- Experience designing and operating cloud-native infrastructure on AWS, particularly Amazon EKS, EC2, S3, ECR, IAM, VPC, and CloudWatch.

- Experience deploying and operating machine learning, deep learning, or generative AI models in production.

- Experience running and troubleshooting GPU-accelerated workloads on Kubernetes, with an understanding of GPU scheduling, utilization, memory constraints, and performance.

- Familiarity with model-serving frameworks such as KServe, NVIDIA Triton Inference Server, vLLM, Ray Serve, TorchServe, or equivalent technologies.

- Strong programming experience in Go or Python, with experience building production-grade APIs, controllers, or distributed backend services.

- Experience with containers, Helm, CI/CD, infrastructure as code, and observability tools such as Prometheus, OpenTelemetry, and Grafana.

- Solid understanding of Linux, networking, storage,



security, and distributed-system fundamentals.

- Strong debugging, problem-solving, communication, and cross-functional collaboration skills.

REQUIRED SKILLS (explicitly call out must-have and nice-to-have skills expectations)

- Experience building an MLOps platform, AI infrastructure platform, internal developer platform, or Kubernetes-based enterprise product.

- Experience writing GPU kernels or performance-critical code using CUDA C/C++ or Triton is a plus.

- Experience with large language model serving, distributed inference, batching, quantization, or inference-performance optimization.

- Experience with NVIDIA GPU Operator, MIG, GPU time-slicing, Dynamic Resource Allocation, or similar GPU-management technologies.

- Familiarity with AWS Inferentia, Trainium, SageMaker, or Amazon Bedrock.

- Experience operating AI platforms across hybrid-cloud, on-premises, air- gapped, or multi-tenant environments.

Pay: ₹1,000,000.00 - ₹3,000,000.00 per year

Application Question(s)

- Immediate to 15 days preferred

Experience

- MLOps: 5 years (Preferred)

Work Location: In person

📌 MLOps Engineer (Coimbatore)
🏢 Sama
📍 Coimbatore