Artificial intelligence has changed the expected tech stack for developers. Knowing how to code is no longer enough: recruiters are looking for profiles capable of designing, integrating, and evaluating systems that rely on language models, data pipelines, and cloud architectures. Understanding what these skills entail allows for targeting the learning that truly matters.
RAG Pipelines and Agent Orchestration: The Technical Foundation That Replaces Simple Prompt Engineering
Many guides limit themselves to prompt writing as the central skill related to language models. This approach is already outdated. Production projects require the design of end-to-end RAG pipelines: document ingestion, vector indexing, query rewriting, and contextualized response delivery.
Mastering retrieval-augmented generation means knowing how to choose a vector database, calibrate the segmentation of source documents, and manage the freshness of indexed data. Without this layer, a language model responds solely based on its parameters, with the hallucinations that this implies.
The orchestration of multi-tool agents is the logical extension. An agent calls external functions, queries APIs, and chains reasoning steps. Developers who can articulate an agent with the MCP (Modular Connection Protocol) to connect heterogeneous tools bring value that prompt engineering alone does not cover. A detailed overview of these key skills on Recrutement Emplois outlines the expected progression towards this type of enhanced profile.
AI Model Evaluations: The Skill Increasingly Mentioned in Job Offers
Deploying a model without measuring its quality is akin to delivering code without unit tests. The design of evaluations (evals) has become a structuring skill for developers working with LLM systems.
An eval, in this context, refers to a set of test cases designed to verify that a generation system produces correct, coherent, and non-toxic responses. The developer defines metrics (relevance, fidelity to sources, absence of hallucination), creates a reference dataset, and automates the execution of tests with each modification of the pipeline.
This discipline is similar to traditional software testing but with an additional challenge: the output of a language model is not deterministic. Two identical executions can produce different responses. Knowing how to build robust evals despite this variability distinguishes an operational developer from one who merely calls an API.
Data and Cloud Skills for AI Developers
Models are only as good as the data that feeds them. A developer integrating artificial intelligence into applications must know how to manipulate data flows at multiple levels:
- Cleaning and transforming raw datasets for training or fine-tuning models, using Python tools like pandas or Polars.
- Managing vector databases (Pinecone, Weaviate, pgvector) to store and query embeddings, a central building block of any RAG pipeline.
- Setting up MLOps pipelines on cloud infrastructures (AWS, GCP, Azure) to automate training, versioning, and deployment of models in production.
Cloud computing is no longer a peripheral skill. The majority of AI projects in companies run on managed cloud services, and a developer who does not know how to configure a containerized deployment environment finds themselves stuck even before going into production.
Python Remains the Pivot Language
Python dominates the AI ecosystem for a simple reason: almost all machine learning, natural language processing, and agent orchestration libraries are written or exposed in Python. Mastering this language beyond the beginner level (generators, static typing, dependency management, packaging) remains a non-negotiable prerequisite.
Security and Ethics of Artificial Intelligence Systems
Companies deploying applications that integrate language models expose themselves to specific risks: prompt injection, exfiltration of confidential data through context, discriminatory biases in generated responses. An AI developer must know how to audit these vulnerabilities just as they secure a REST API.
The European regulation on AI imposes transparency and traceability obligations for certain categories of systems. Understanding the main lines of this regulatory framework allows for anticipating compliance constraints from the design phase, rather than discovering them at the time of production.
Practically, this translates into technical choices: systematic logging of model inputs and outputs, implementation of content filters, documentation of training datasets. These tasks do not fall under a legal team; they are part of the code.
Training and Technical Monitoring: Structuring Skill Development
The training offer in artificial intelligence has exploded, making it difficult to sift through. A few criteria help distinguish useful paths from cosmetic certifications:
- The program includes practical projects with deployment, not just Jupyter notebooks.
- Instructors cover MLOps and model evaluation, not just training.
- The curriculum integrates manipulation of real data, with its imperfections (missing values, sampling biases, heterogeneous formats).
Daily technical monitoring is part of the job. Agent orchestration libraries, vector databases, and model APIs evolve at a pace that renders unmaintained knowledge obsolete within months. Following the GitHub repositories of major projects and reading release notes remains the most reliable way to stay current.
The sought-after developer profile in 2024 combines a solid foundation in Python code, operational understanding of RAG pipelines and agents, the ability to rigorously evaluate an AI system, and sufficient cloud culture to deploy it all. These skills complement each other more than they overlap, and it is their articulation that creates value in the job market.