AI engineering teams replacing fragile hand-tuned string prompts with programmatic, compiler-optimized LLM pipelines frequently search to hire a freelance DSPy (`MIPROv2`, `BootstrapFewShot`), automated prompt optimization & Compound AI Systems programmer. I am Aldian Fazrihady, a Senior Freelance AI Application Engineer and Python Architect available via Upwork.
Core DSPy & Programmatic LLM Optimization Capabilities
- Declarative Signatures & Modules (`dspy.Signature`, `ChainOfThought`, `ReAct`): Decoupling task specification from model-specific prompt strings so switching from GPT-4o to Claude 3.7 Sonnet, Gemini 2.5 Flash, or Llama 3.3 requires recompiling rather than rewriting prompts by hand.
- Metric-Driven Optimizer Compilation (`MIPROv2` & `BootstrapFewShotWithRandomSearch`): Automatically searching over instruction candidates and few-shot demonstrations against your exact domain evaluation metric.
- Multi-Stage Compound AI Pipelines: Optimizing multi-hop RAG query rewriters, rerankers, and answer synthesizers jointly end-to-end.
Frequently Asked Questions
Why does DSPy outperform manual prompt engineering when migrating between LLM providers?
Each model family responds differently to formatting and instruction phrasing. DSPy treats prompts and few-shot examples as learnable weights optimized directly against a validation dataset.
How can we hire you as a freelance DSPy & Compound AI programmer?
Message me on Upwork.