Robotics technology LLC
Job Summary:
We are looking for a highly skilled Ops Engineer with strong expertise in Terraform, Databricks, MLOps, and Large Language Models (LLMs This role is ideal for someone passionate about automating infrastructure, optimizing data and machine learning workflows, and supporting the deployment of advanced AI models in production environments. You will work closely with data scientists, ML engineers, and DevOps teams to enable scalable, secure, and reliable ML and LLM infrastructure.
Required Skills:
Strong experience with Terraform (Cloud/Open Source) for cloud infrastructure automation.
Proficiency with Databricks: clusters, jobs, notebooks, DBFS, and workspace management.
Hands-on experience with MLOps tools (e.g., MLflow, TFX, Kubeflow, Airflow, or SageMaker Pipelines
Experience deploying and scaling Large Language Models (LLMs) in production environments.
Proficiency in CI/CD tooling (GitHub Actions, Azure DevOps, GitLab CI, etc.
Scripting and automation using Python, Bash, or similar languages.
Knowledge of cloud services (AWS, Azure, or GCP), especially for ML workloads.
Understanding of containerization (e.g., Docker) and orchestration (e.g., Kubernetes) for model deployments.
Strong problem-solving, troubleshooting, and documentation skills.
We are looking for a highly skilled Ops Engineer with strong expertise in Terraform, Databricks, MLOps, and Large Language Models (LLMs This role is ideal for someone passionate about automating infrastructure, optimizing data and machine learning workflows, and supporting the deployment of advanced AI models in production environments. You will work closely with data scientists, ML engineers, and DevOps teams to enable scalable, secure, and reliable ML and LLM infrastructure.
Required Skills:
Strong experience with Terraform (Cloud/Open Source) for cloud infrastructure automation.
Proficiency with Databricks: clusters, jobs, notebooks, DBFS, and workspace management.
Hands-on experience with MLOps tools (e.g., MLflow, TFX, Kubeflow, Airflow, or SageMaker Pipelines
Experience deploying and scaling Large Language Models (LLMs) in production environments.
Proficiency in CI/CD tooling (GitHub Actions, Azure DevOps, GitLab CI, etc.
Scripting and automation using Python, Bash, or similar languages.
Knowledge of cloud services (AWS, Azure, or GCP), especially for ML workloads.
Understanding of containerization (e.g., Docker) and orchestration (e.g., Kubernetes) for model deployments.
Strong problem-solving, troubleshooting, and documentation skills.