Hire an AI Developer in Morocco — LLM, RAG & ML Integration Experts

AI engineers who build LLM-powered products, RAG pipelines, computer vision and ML integrations.

ProfileAI Developer
Experience2–6 years experience
AvailabilityAvailable within 1 week

Technical skills

  • LangChain / LlamaIndex for LLM application orchestration
  • OpenAI API, Anthropic Claude, Mistral — prompt engineering
  • RAG (Retrieval-Augmented Generation) with vector databases (Pinecone, Weaviate, pgvector)
  • Fine-tuning: LoRA/QLoRA on open-source models (Llama, Mistral)
  • Computer vision: YOLO, SAM, OpenCV for detection and segmentation
  • MLOps: model deployment (FastAPI + Docker), monitoring, A/B testing

Typical projects

AI chatbot with RAG — document Q&A, knowledge base assistant
Automated document processing (contracts, invoices, medical records)
Product recommendation engine using embeddings
AI-powered search (semantic search replacing keyword search)
Custom LLM fine-tuning for domain-specific tasks

Tech stack

PythonLangChainOpenAIPineconeFastAPIHugging FaceDockerpgvector

Why hire via Tuplify Studio

Vetted profiles only

Every developer passes a technical assessment. You get senior profiles — not juniors resold as seniors.

Start within 1 week

No 3-month recruitment process. Developers are available and integrated within a week.

Replacement guarantee

If a profile doesn't fit, we replace within 7 days at no extra cost. Your project keeps moving.

Simple engagement

Fixed price or T&M. Monthly invoices. No social charges, no long-term commitment unless you want it.

Engagement options

Dedicated full-time

One or more developers working exclusively on your project, 5 days/week.

Part-time (20h/week)

Ideal for maintenance, iterative additions or when you don't need full-time capacity.

Fixed-price project

A defined scope, a price, a deadline. Best for one-shot projects or new features.

Frequently asked questions

Multi-layer guardrails: (1) grounding via RAG so answers are always based on known documents, (2) confidence thresholds that trigger human escalation for uncertain answers, (3) output validation against known schemas, (4) red-teaming tests before launch.

pgvector if you already use PostgreSQL (zero new infra, SQL queries work). Pinecone for managed cloud with auto-scaling. Weaviate for multi-modal search. Qdrant for high-performance self-hosted. For most RAG projects under 1M documents: pgvector is the pragmatic choice.

Yes — we deploy Ollama, llama.cpp or vLLM on your infrastructure for air-gapped deployments. Common setups: Mistral 7B / Llama 3 on a GPU server. Performance is lower than GPT-4 but data never leaves your network.

We define measurable KPIs before build: answer accuracy (evaluated vs ground truth), topic coverage rate, fallback rate (how often the AI escalates), latency per query, and cost per query. These are tracked in a monitoring dashboard in production.

Ready to hire? Let's talk.

30-minute call. We present matching profiles within 48 hours. No commitment required.