Hire AI/ML Engineers — Custom Models, LLMs & Production AI

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Hire AI/ML Engineers — Production AI, Not Demos

The gap between an impressive AI demo and a system that reliably does useful work in production is large. Most AI projects stall at the prototype stage because the data wasn’t ready, the evaluation criteria weren’t defined, or the deployment architecture wasn’t planned.

CimpleO’s AI/ML engineers handle the full lifecycle: problem framing, data preparation, model development, evaluation, deployment, and monitoring. We build AI systems that your business can actually use, not proof-of-concepts that sit on a laptop.

What We Build

Predictive Analytics & Forecasting ML models trained on your historical data for demand forecasting, churn prediction, revenue modelling, inventory optimisation, and risk scoring. Integrated into your existing dashboards and workflows — not a standalone tool your team has to learn separately.

NLP & Document Processing Document classification, information extraction from contracts/invoices/forms, sentiment analysis, topic modelling, and named entity recognition. Fine-tuned transformer models (BERT, DistilBERT, domain-specific variants) or LLM-based pipelines depending on your accuracy and cost requirements.

RAG Systems & LLM Integration Retrieval-Augmented Generation systems that let your teams query internal knowledge bases in plain language — product documentation, support ticket history, policies, research papers. We build the full pipeline: document ingestion, chunking, embedding, vector store (Pinecone, Weaviate, pgvector), and retrieval chain. Grounded answers, not hallucinations.

Computer Vision Object detection, image classification, defect identification, OCR, and document understanding. Models trained on your images — not generic datasets. Deployable on cloud infrastructure or edge hardware (Jetson, Raspberry Pi, STM32) for real-time inference in the field.

Recommendation Systems Collaborative filtering, content-based filtering, and hybrid approaches for product recommendations, content personalisation, and search result ranking. We’ve built recommendation systems for eCommerce platforms handling tens of thousands of products and millions of interactions.

MLOps & AI Infrastructure Model versioning with MLflow or DVC, automated retraining pipelines, A/B testing infrastructure for model comparison, drift detection and monitoring in production, and CI/CD for ML workflows. We build the infrastructure that keeps your models accurate over time, not just at launch.

Technology Stack

Frameworks: PyTorch, TensorFlow, Keras, scikit-learn, Hugging Face Transformers
LLM: OpenAI, Anthropic, LLaMA 3, Mistral, Qwen, LangChain, LlamaIndex
Vector databases: Pinecone, Weaviate, Qdrant, pgvector (PostgreSQL)
Computer vision: OpenCV, YOLO, Detectron2, TensorFlow Object Detection API
Data: Pandas, Polars, DVC, Apache Airflow, Kafka, Spark
MLOps: MLflow, Weights & Biases, Evidently AI, BentoML, Seldon
Cloud: AWS SageMaker, Azure ML, GCP Vertex AI, or self-hosted

Engagement Models

Fixed-scope project: defined ML problem, agreed evaluation criteria, fixed price. Best for focused prediction tasks with clear success metrics.

AI product development: full feature including data pipeline, model, API, and frontend integration. One team, end-to-end.

MLOps retainer: ongoing model monitoring, retraining, and improvement as your data evolves. Most production models need this — data drift makes models degrade without it.

AI audit: assessment of an existing AI system’s performance, data quality, and architecture. Useful when results aren’t meeting expectations.

For LLM-specific integrations (ChatGPT API, RAG, chatbots), see ChatGPT & LLM Development Services. For AI-powered IoT edge computing, see IoT Engineering.

Get a Scope

Tell us your use case, what data you have, and what “good enough” looks like for your application. We’ll respond within 24 hours with a realistic assessment.

Frequently Asked Questions

When should I hire a custom AI/ML engineer vs just using ChatGPT API?

Use the ChatGPT/Claude API when your use case is general-purpose language tasks — summarisation, classification, Q&A on your documents (RAG). Hire a custom ML engineer when your task requires training on your proprietary data (fraud detection on your transaction history, defect classification on your product images), when API inference cost at scale is prohibitive, or when data privacy prevents sending data to an external API.

How much does custom AI/ML development cost?

A focused ML model (single prediction task, clean labelled data): $20,000–$50,000. A full AI feature with data pipeline, model training, deployment infrastructure, and monitoring: $50,000–$150,000. RAG-based LLM systems: $25,000–$70,000. We scope the data situation first — poor data quality can double the timeline.

Do you handle data preparation, or do we need clean training data?

We handle data preparation: collection, cleaning, labelling strategy, feature engineering, and augmentation. If your data is genuinely too sparse or noisy to produce a useful model, we tell you upfront — not after 3 months of billing.

Can you deploy AI models on our own infrastructure?

Yes. For environments where data can't leave your servers, we deploy open-source models (LLaMA 3, Mistral, Phi-3, Qwen) on your hardware or private cloud. We benchmark model quality against your specific tasks and give you a comparison with hosted APIs before you commit to on-prem.

How do you measure if a model is working well enough?

We define evaluation metrics at the start — precision, recall, F1, BLEU, ROUGE, or task-specific metrics depending on the problem. We evaluate on a holdout set that wasn't used in training. For LLM systems, we measure hallucination rate and answer accuracy systematically before going live.

Can you integrate AI into our existing application?

Yes. Most AI integrations are additive — a new API endpoint, a background processing pipeline, or a new UI component. We design integrations to minimise disruption to your existing system. The majority of our AI work doesn't require rebuilding your stack.