Senior MLOps Engineer

- Proficiency with AWS services (EKS, EC2, S3, RDS, CloudWatch, IAM). - Solid experience with Python, Docker, Git, and CI/CD pipelines. - Strong understanding of model lifecycle management, data pipelines, and observability tools (Grafana, Prometheus, Loki).

As a Senior MLOps Engineer, you will design, build, and maintain robust ML(Machine Learning) infrastructure across training, inference, and deployment pipelines. You will take ownership of the model lifecycle — from data ingestion to real-time serving — and ensure our LLM and speech models are deployed efficiently, securely, and reproducibly in Kubernetes-based environments. This position requires deep hands-on experience with Kubernetes (EKS), Helm, AWS cloud infrastructure, and modern MLOps toolchains (e.g., vLLM, SGLang, OpenWebUI, Weights & Biases, MLflow). Familiarity with speech/voice AI frameworks like ElevenLabs, Whisper, and RVC is also valuable. Key Responsibilities

Key Responsibilities - Design and manage scalable ML infrastructure on AWS using EKS, EC2, RDS, S3, and IAM-based access control. - Build and maintain Kubernetes deployments for LLM and TTS inference using Helm, ArgoCD, and Prometheus/Grafana monitoring.

- Implement and optimize model serving pipelines using vLLM, SGLang, TensorRT, or similar frameworks for high-throughput inference. - Develop CI/CD and MLOps automation for data versioning, model validation, and deployment (GitHub Actions, Jenkins, or AWS CodePipeline). - Integrate OpenWebUI, Gradio, or similar UIs for user-facing model demos and internal evaluation tools.

- Experience deploying ML models via vLLM, SGLang, TensorRT, or Ray Serve. - Proficiency with AWS services (EKS, EC2, S3, RDS, CloudWatch, IAM). - Solid experience with Python, Docker, Git, and CI/CD pipelines.

- Design and manage scalable ML infrastructure on AWS using EKS, EC2, RDS, S3, and IAM-based access control. - Build and maintain Kubernetes deployments for LLM and TTS inference using Helm, ArgoCD, and Prometheus/Grafana monitoring. - Implement and optimize model serving pipelines using vLLM, SGLang, TensorRT, or similar frameworks for high-throughput inference.

- Solid experience with Python, Docker, Git, and CI/CD pipelines. - Strong understanding of model lifecycle management, data pipelines, and observability tools (Grafana, Prometheus, Loki). - Excellent collaboration skills with ML researchers and software engineers.

The Role As a Senior MLOps Engineer, you will design, build, and maintain robust ML(Machine Learning) infrastructure across training, inference, and deployment pipelines. You will take ownership of the model lifecycle — from data ingestion to real-time serving — and ensure our LLM and speech models are deployed efficiently, securely, and reproducibly in Kubernetes-based environments. This position requires deep hands-on experience with Kubernetes (EKS), Helm, AWS cloud infrastructure, and modern MLOps toolchains (e.g., vLLM, SGLang, OpenWebUI, Weights & Biases, MLflow). Familiarity with speech/voice AI frameworks like ElevenLabs, Whisper, and RVC is also valuable.

- 4+ years of experience in MLOps, DevOps, or Cloud Infrastructure Engineering for ML systems. - Strong proficiency in Kubernetes, Helm, and container orchestration. - Experience deploying ML models via vLLM, SGLang, TensorRT, or Ray Serve.

- Familiarity with multi-GPU scheduling, NCCL optimization, and HPC cluster integration. - Knowledge of security, cost management, and network policy in multi-tenant Kubernetes clusters and cloudflare systems. - Prior work in LLM deployment, fine-tuning pipelines, or foundation model research.

- Contribute to internal tools for dataset curation, model monitoring, and retraining pipelines. - Maintain infrastructure-as-code using Terraform and Helm charts for reproducibility and governance. - Support real-time multimodal workloads (voice, text, vision) across inference clusters.