LLMOps Engineering

Implement branching strategies, code review workflows, and merge policies for AI projects.

Build CI/CD pipelines using Argo Workflows for prompts, model configs, RAG configs, and guardrail policies.

Build a Python tool that initializes a Git repository with trunk-based development conventions. Create a standard AI project directory structure (prompts, model-configs, eval-datasets, k8s), configure Git settings for rebase-by-default and autostash, deterministically classify candidate branches…

Build a Python validator that enforces feature branch naming conventions for AI projects. Validate branch names against patterns like feature/, fix/, config/, infra/, deploy/, eval/. Parse branch names to extract type and description components. Report invalid branches with helpful error messages…

Build a Python tool that manages the complete lifecycle of a feature branch: creation from main, commit tracking, squash merge simulation, and cleanup. Track branch metadata including age, commit count, and files changed. Detect stale branches that exceed the maximum age threshold.

Build a Python tool that deterministically generates and audits pre-commit hook configurations for AI projects. Render .pre-commit-config.yaml with hooks for ruff, mypy, yamllint, detect-secrets, and JSON validation, validate prompt template files against a Pydantic schema, and audit a rendered…

Build a Python tool that generates Renovate configuration and Kubernetes CronJob manifests for automated dependency management. Create renovate.json with AI-specific package grouping rules. Generate the Kubernetes YAML for deploying Renovate as a GKE CronJob.

Build a Python tool that compares Renovate and Dependabot across multiple dimensions for AI project dependency management. Generate comparison matrices, score each tool on relevant criteria, and produce a recommendation based on project requirements.

Build a simulated Argo Workflow pipeline for prompt CI/CD operations including linting, validation, evaluation with LLM calls, and promotion stages. Implement pipeline orchestration with artifact storage and metrics tracking for GenAI operations.