AI Engineering
Bring AI to Life, Without the Headaches
AI engineering takes a model from proof-of-concept to a production system that is reliable, observable, and actually used by your team. I specialise in retrieval-augmented generation (RAG) pipelines, LLM integrations, and agentic workflows built with LangChain, Databricks, and cloud-native infrastructure. The focus is always on measurable business impact, not model novelty.
AI isn’t magic, it’s engineering. I help businesses build real, usable AI systems that automate tasks, surface predictions, and unlock insights, all while staying compliant and grounded in business value.
What You Get
- Technical scoping document with chosen AI pattern (RAG / agent / fine-tune)
- Vector ingestion pipeline with chunking and embedding strategy documented
- Retrieval-augmented API endpoint with evaluation harness (RAGAS or custom)
- MLflow experiment tracking and model registry setup
- Monitoring dashboard (latency, token cost, retrieval quality)
- Model card and operational runbook
Common AI Challenges I Solve
Prototype that never makes it to production
The gap between a Jupyter notebook and a monitored, versioned, production ML service is enormous. I handle MLflow experiment tracking, containerization, CI/CD, and Databricks serving infrastructure so your model ships.
LLM hallucinations making the output unusable
RAG pipelines reduce hallucination rates by 60–80% compared to vanilla prompting, by grounding answers in your actual documents and data. I design the retrieval layer, chunking strategy, and evaluation harness.
No way to measure if AI is actually helping
Without an eval framework, you can't tell if the next prompt change made things better or worse. I define task-specific metrics (retrieval recall, answer faithfulness, latency P95) and wire them into a dashboard from week one.
My AI Engineering Building Blocks
RAG-first for enterprise knowledge
For most business use cases, retrieval beats fine-tuning on cost, speed, and maintainability. I design vector pipelines on Databricks Vector Search or pgvector that keep knowledge bases current without retraining.
Evaluation before deployment
I build an LLM evaluation suite (using frameworks like RAGAS or custom judges) before the first model goes live. Regression testing every deployment.
Observability for AI
Latency, token costs, retrieval quality, and user feedback signals feed into a live dashboard. You can see model drift before it affects users.
My Approach
Use-case scoping + data readiness (weeks 1–2)
We define the AI task, assess whether RAG, fine-tuning, or an agent is the right pattern, and audit data availability. Output: a one-page technical spec.
Proof of concept + eval baseline (weeks 2–6)
I build the PoC and instrument a baseline eval harness. You can measure quality from day one.
Production build + MLOps wiring (weeks 6–14)
Containerized serving, CI/CD, feature pipelines, and monitoring. Everything versioned and observable.
Handover + model card (final 1–2 weeks)
I document the architecture, evaluation results, and operational runbook. Your team owns it.
Glossary
- RAG (Retrieval-Augmented Generation)
- A pattern that improves LLM accuracy by retrieving relevant documents from a knowledge base at query time and injecting them into the prompt reducing hallucinations without retraining the model.
- LLM (Large Language Model)
- A neural network trained on large text corpora to generate, summarise, classify, and reason over language. Examples: GPT-4, Claude, Llama 3.
- Embeddings
- Numerical vector representations of text that encode semantic meaning similar concepts end up geometrically close. Used to power similarity search in RAG pipelines.
- Vector store
- A database optimised for storing and querying embeddings by similarity rather than exact match. Examples: Pinecone, pgvector, Chroma, Databricks Vector Search.
- Fine-tuning
- Continuing training a pre-trained model on a smaller domain-specific dataset to improve accuracy on a narrow task, as opposed to prompting or RAG which leave model weights unchanged.
Common Questions
What is RAG and when should I use it instead of fine-tuning?
RAG (Retrieval-Augmented Generation) retrieves relevant documents at query time and injects them into the LLM prompt. Use RAG when your knowledge base changes frequently (product docs, internal wikis, customer data) and you need answers to be grounded in specific sources. Fine-tuning is better for changing the model's style, tone, or domain vocabulary not for keeping it up to date.
How long does it take to build a production RAG pipeline?
A focused RAG implementation document ingestion, embedding pipeline, vector store, retrieval logic, and a tested API endpoint typically takes 8–12 weeks from scoping to production. Adding an agentic layer (tool use, multi-step reasoning) adds another 4–6 weeks.
What does an AI engineering engagement cost?
I price on scope, not time-and-materials, so you know the fixed cost before work starts. Pricing varies significantly depending on whether you need a focused RAG MVP or a full agentic platform with MLOps infrastructure. Reach out for a scoping call and I'll provide a detailed estimate.
Ready to Build Better Data Systems?
Let's discuss how I can help you modernize your data infrastructure and unlock the full potential of your data.
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