Job Description

About Us At Valiance, we are building next-generation AI solutions to solve high-impact business problems. As part of our AI/ML team, you’ll work on deploying cutting-edge Gen AI models, optimizing performance, and enabling scalable experimentation. Role Overview We are looking for a skilled MLOps Engineer with hands-on experience in deploying open-source Generative AI models on cloud and on-prem environments. The ideal candidate should be adept at setting up scalable infrastructure, observability, and experimentation stacks while optimizing for performance and cost. Responsibilities Deploy and manage open-source Gen AI models (e.g., LLaMA, Mistral, Stable Diffusion) on cloud and on-prem environments Set up and maintain observability stacks (e.g., Prometheus, Grafana, OpenTelemetry) for monitoring Gen AI model health and performance Optimize infrastructure for latency, throughput, and cost-efficiency in GPU/CPU-intensive environments Build and manage an experimentation stack to enable rapid testing of various open-source Gen AI models Work closely with ML scientists and data teams to streamline model deployment pipelines Maintain CI/CD workflows and automate key stages of the model lifecycle Leverage NVIDIA tools (Triton Inference Server, TensorRT, CUDA, etc.) to improve model serving performance (preferred) Required Skills & Qualifications Strong experience in deploying ML/Gen AI models using Kubernetes, Docker, and CI/CD tools Proficiency in Python, Bash scripting, and infrastructure-as-code tools (e.g., Terraform, Helm) Experience with ML observability and monitoring stacks Familiarity with cloud services (GCP, AWS, or Azure) and/or on-prem environments Exposure to model tracking tools like MLflow, Weights & Biases, or similar Bachelor’s/Master’s in Computer Science, Engineering, or related field Nice to Have Hands-on experience with NVIDIA ecosystem (Triton, CUDA, TensorRT, NGC) Familiarity with serving frameworks like vLLM, DeepSpeed, or Hugging Face Transformers Show more Show less