AI / Generative AI (LLM)TCS accelerates AI adoption for enterprise clients (industry, energy, services). We are looking for a passionate Generative AI intern to join our Toulouse team. You will work in a structured project environment (Agile), mentored by an AI Tech Lead, and contribute to GenAI POCs and MVPs that move toward production.
Missions :
Design and develop AI/GenAI POCs (RAG, agents, assistants, domain copilots) and evolve them into production-ready MVPs.
Build LLM chains (prompting, tooling, evaluation) and agents (tools, planning, multi-step execution).
Implement data pipelines for preparation, indexing, and search (hybrid BM25 + vectors).
Set up evaluation & observability (quality, cost, latency, hallucination rate, tracing).
Deploy on GCP (preferred) – Vertex AI, Cloud Run, BigQuery, IAM – or, depending on projects, AWS/Azure.
Write technical documentation, prepare demos, and present your work in sprint reviews.
Stack & technical environment (reference) :
Language: Python (required, excellent level).
AI / DL: PyTorch or TensorFlow, scikit-learn, NumPy/Pandas.
LLM / GenAI:
Models & tools: Gemini (Vertex AI), open-source (Llama, Mistral…), embeddings, tokenizers.
Orchestration: LangChain / LlamaIndex / Haystack, LangGraph or agentic frameworks (AutoGen, CrewAI).
RAG: ingestion (PDF/HTML), chunking, encoding, Vector DB (FAISS, pgvector, Pinecone/Weaviate as needed), reranking (BM25/ColBERT).
Guardrails & evaluation: Ragas / DeepEval / promptfoo, Pydantic validations, safety rules, basic red-teaming.
Cloud & Dev :
GCP (preferred): Vertex AI, BigQuery, Cloud Storage, Cloud Run/Functions, Pub/Sub, Secret Manager, IAM.
Alternatives: AWS (Bedrock, S3, Lambda), Azure (AI Studio, OpenAI, Functions).
MLOps / DevOps :
Docker, Git/GitHub, CI/CD (GitHub Actions/GitLab CI), testing (pytest), pre-commit, packaging (poetry).
Nice-to-have: basic TypeScript/React for small demo UIs, OpenAPI/REST, security basics (PII, GDPR), LangFuse/Arize Phoenix for tracing.
TCS accelerates AI adoption for enterprise clients (industry, energy, services). We are looking for a passionate Generative AI intern to join our Toulouse team. You will work in a structured project environment (Agile), mentored by an AI Tech Lead, and contribute to GenAI POCs and MVPs that move toward production.
Missions :
Design and develop AI/GenAI POCs (RAG, agents, assistants, domain copilots) and evolve them into production-ready MVPs.
Build LLM chains (prompting, tooling, evaluation) and agents (tools, planning, multi-step execution).
Implement data pipelines for preparation, indexing, and search (hybrid BM25 + vectors).
Set up evaluation & observability (quality, cost, latency, hallucination rate, tracing).
Deploy on GCP (preferred) – Vertex AI, Cloud Run, BigQuery, IAM – or, depending on projects, AWS/Azure.
Write technical documentation, prepare demos, and present your work in sprint reviews.
Stack & technical environment (reference) :
Language: Python (required, excellent level).
AI / DL: PyTorch or TensorFlow, scikit-learn, NumPy/Pandas.
LLM / GenAI:
Models & tools: Gemini (Vertex AI), open-source (Llama, Mistral…), embeddings, tokenizers.
Orchestration: LangChain / LlamaIndex / Haystack, LangGraph or agentic frameworks (AutoGen, CrewAI).
RAG: ingestion (PDF/HTML), chunking, encoding, Vector DB (FAISS, pgvector, Pinecone/Weaviate as needed), reranking (BM25/ColBERT).
Guardrails & evaluation: Ragas / DeepEval / promptfoo, Pydantic validations, safety rules, basic red-teaming.
Cloud & Dev :
GCP (preferred): Vertex AI, BigQuery, Cloud Storage, Cloud Run/Functions, Pub/Sub, Secret Manager, IAM.
Alternatives: AWS (Bedrock, S3, Lambda), Azure (AI Studio, OpenAI, Functions).
MLOps / DevOps :
Docker, Git/GitHub, CI/CD (GitHub Actions/GitLab CI), testing (pytest), pre-commit, packaging (poetry).
Nice-to-have: basic TypeScript/React for small demo UIs, OpenAPI/REST, security basics (PII, GDPR), LangFuse/Arize Phoenix for tracing.
Profile :
Master’s/Grande École/Engineering student specializing in AI/ML/Data/Software.
Comfortable with Generative AI and LLMs, with at least one hands-on POC experience (internship, project, hackathon).
Comfortable with GCP (preferred) or another cloud (AWS/Azure).
Strong Python skills and software craft practices (tests, reviews, clean code).
Analytical mindset, curiosity, autonomy, product thinking, and clear communication in French (technical English appreciated).
What we offer :
Mentorship by an AI Tech Lead, code reviews, and methodological coaching.
Real-world projects with high visibility at major enterprises.
Access to a fully equipped ecosystem (cloud, vector DB, tracing, CI/CD).
Agile ways of working, team rituals, sharing culture, internal talks & tech watch.
Expected deliverables by end of internship :
1–2 functional POC/MVPs (agents/RAG/copilot).
Technical package (architecture, decisions, costs, risks), tests & evaluation metrics.
Demo and knowledge transfer to the team.