AI Systems Consulting: Python & TypeScript
Budget: $8 – $15 USD
I’m looking for a seasoned engineer who can jump in as a thought-partner while I build out a set of AI-driven services written in Python (Backend) and TypeScript + React (Front-end). The work is wide-ranging, so the engagement will be structured as ad-hoc consulting sessions plus short, focused code reviews or proof-of-concept implementations whenever I hit a road-block.
Here’s what I expect we’ll tackle together:
• Algorithm optimisation — from profiling tight loops in Python to squeezing latency out of TypeScript back-ends.
• Debugging and troubleshooting thorny runtime issues that slip past unit tests.
• Feature development advice, particularly around RAG pipelines, LLM workflows, and agent orchestration.
• Deployment guidance: GitHub branching strategies, automated CI/CD, and pragmatic cloud infrastructure choices (containerisation, serverless, etc.).
• Secure user-authentication patterns suitable for AI services, plus structured logging and test coverage that won’t crumble under real traffic.
Typical session flow
1. I share code snippets or a branch link.
2. You walk me through potential fixes or improvements on a live call or via concise review comments.
3. We wrap up with a short actionable checklist I can implement immediately.
Deliverables
• Concise recommendations or sample code that cleanly drop into the existing repo.
• Clear reasoning behind each suggestion so the decision-making process is documented.
• When relevant, a working prototype (Jupyter notebook, small TS module, or CI/CD YAML) that demonstrates the concept.
If you’re fluent in both Python and the modern TypeScript ecosystem, keep up with transformer-based stacks, and enjoy teaching as much as coding, let’s set up our first session.
Here’s what I expect we’ll tackle together:
• Algorithm optimisation — from profiling tight loops in Python to squeezing latency out of TypeScript back-ends.
• Debugging and troubleshooting thorny runtime issues that slip past unit tests.
• Feature development advice, particularly around RAG pipelines, LLM workflows, and agent orchestration.
• Deployment guidance: GitHub branching strategies, automated CI/CD, and pragmatic cloud infrastructure choices (containerisation, serverless, etc.).
• Secure user-authentication patterns suitable for AI services, plus structured logging and test coverage that won’t crumble under real traffic.
Typical session flow
1. I share code snippets or a branch link.
2. You walk me through potential fixes or improvements on a live call or via concise review comments.
3. We wrap up with a short actionable checklist I can implement immediately.
Deliverables
• Concise recommendations or sample code that cleanly drop into the existing repo.
• Clear reasoning behind each suggestion so the decision-making process is documented.
• When relevant, a working prototype (Jupyter notebook, small TS module, or CI/CD YAML) that demonstrates the concept.
If you’re fluent in both Python and the modern TypeScript ecosystem, keep up with transformer-based stacks, and enjoy teaching as much as coding, let’s set up our first session.
Related categories:
Python
Algorithm
Software Architecture
React.js
Typescript
Cloud
AI Consulting
AI Model Development
AI Development