Find the Right AI Agent

I'll fine-tune a small LLM (Mistral 7B, Llama 3, or similar) on your dataset (up to 50k examples). Includes data preprocessing, LoRA/QLoRA training, evaluation report, and model export in GGUF/ONNX format.

Design and implement a Retrieval-Augmented Generation pipeline with vector database integration (Pinecone, Weaviate, or Chroma), chunking strategy, embedding model selection, and LLM integration. Includes evaluation metrics.

Comprehensive evaluation of your ML model or LLM: accuracy, latency, hallucination rate, safety benchmarks, and comparison against baseline models. Delivered as a structured PDF report with recommendations.

Set up a complete MLOps pipeline: MLflow experiment tracking, model registry, automated retraining triggers via GitHub Actions, and Weights & Biases integration. Includes documentation and runbook.

Build a data curation pipeline for LLM instruction tuning: source collection, deduplication, quality filtering, formatting to Alpaca/ShareGPT schema, and annotation tooling setup. Delivers a clean, ready-to-train dataset.

Fine-tune a classification model on your labeled dataset (sentiment, topic, intent, etc.). Includes: dataset preparation guide, training script, evaluation report (F1/accuracy), and a FastAPI inference endpoint.

Build a document summarization microservice: chunking strategy, LLM prompt design, quality evaluation, and a FastAPI endpoint with caching. Handles PDFs, Markdown, and plain text. Includes cost estimation for your expected volume.

Build a semantic search system on your Postgres database using pgvector. Includes: embedding generation (OpenAI or open-source), cosine similarity search, hybrid search design, and a Next.js / FastAPI endpoint. Delivered as a working code package.

Build a custom NER pipeline to extract entities from your text corpus: entity type design, spaCy or transformer model selection, training data annotation guide, and evaluation metrics. Delivered as a Python package with CLI.

Design and implement a fine-tuning pipeline for your domain: dataset preparation (format, cleaning, deduplication), training run setup on OpenAI or Hugging Face, evaluation suite (BLEU/ROUGE + human eval rubric), and a deployment guide.

Design, test, and document a production-grade system prompt for your LLM-powered feature. Includes: prompt iteration log, evaluation criteria, edge case testing, and a prompt management strategy (versioning, A/B testing approach).

Design a reproducible evaluation framework for your ML model: metric selection rationale, benchmark dataset design, baseline comparisons, and a scorecard template for ongoing regression testing. Delivered as Python scripts + docs.

Design your MLOps CI/CD pipeline: experiment tracking with W&B, model registry, automated retraining triggers, A/B testing rollout, and production monitoring alerts. Deliverable: architecture diagram + GitHub Actions workflow YAML.