Freelancer profile translated to English.
Back to original languageAbout Bakary
Senior LLMOps & MLOps Engineer (5+ years) – Specialized in RAG and Generative AI systems in production (AWS, GCP)
I design and deploy AI systems in production (RAG, LLMs, ML) for critical use cases, with a focus on performance, scalability, and cost optimization.
Core Expertise:
• Production-ready RAG architecture (hybrid search, reranking, vector DB: Qdrant, Pinecone)
• LLM Industrialization (vLLM, GPU deployment, scalable APIs)
• End-to-end MLOps (CI/CD, Prefect/Airflow orchestration, monitoring & drift)
Impact:
• Improved accuracy of semantic search systems
• Reduced LLM inference latency and costs
• Deployment of robust and automated ML pipelines
Stack:
LLMOps: LangChain, LangGraph, vLLM, Hugging Face
Vector DB: Qdrant, Pinecone, FAISS
Cloud: AWS (SageMaker, S3, Lambda), GCP (Vertex AI)
Data/ML: PyTorch, XGBoost, PySpark
Available for projects:
• RAG / Generative AI
• MLOps / ML Industrialization
• LLM production system optimization
French
Native or bilingual
English
Fluent
Can work on-site
Choisy-le-Roi (up to 50km)
Experience
- Base Claude BernardLead Data & AIOctober 2025 - Today (8 months)Île-de-France, France• Design and deployment of a medical RAG system in production (880k+ documents), improving response accuracy and ensuring traceability (sourced citations)• Implementation of a multi-step retrieval pipeline:- Query validation via LLM (medical filtering)- Multi-query expansion (semantic coverage)- Hybrid search (Qdrant: dense BGE-M3 + sparse Splade + fusion)- Reranking via cross-encoder for high clinical accuracy• LLM Industrialization:- vLLM deployment (OpenAI-like API) on GPU (RunAI)- Asynchronous processing (Celery/Redis) → latency reduction• Implementation of a production LLMOps stack:- GitLab CI/CD, Docker containerization, monitoring- Reliability, reproducibility, and cost optimization• Development of incremental data pipelines (Prefect):- Multi-source ingestion (ANSM, HAS…)- Intelligent versioning (hash) → recalculation reductionStack: Qdrant, vLLM, LangChain, Prefect, FastAPI, Docker, GitLab CI/CD, PostgreSQL
- LisiData & MLOps EngineerCONSULTING AND AUDITSNovember 2022 - Today (3 years and 7 months)Paris, France
- • Development of end-to-end MLOps pipelines on AWS SageMaker:
- GitLab CI/CD, automated deployment, model registry- Drift monitoring with alerts → robustness improvement• Design of scalable data pipelines:- ETL (AWS Glue, PySpark), orchestration (Airflow)- S3 Data Lake + analytics (Athena)• Development of APIs and access security:- AWS Lambda + API Gateway- User management via Cognito• Implementation of an industrial RAG system:- Semantic search on technical documentation- Observability (LangSmith): latency, costs, hallucinationsStack: AWS (SageMaker, Glue, Airflow, Lambda, S3), LangChain, OpenSearch, PySpark - TradeInData ScientistApril 2021 - October 2022 (1 year and 6 months)Paris, France️ Stack: AWS (S3, Textract, QuickSight, SageMaker, Lambda, Athena), Airflow, FastAPI, XGBoost, PostgreSQL, PySpark
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Education
- Master of ScienceÉcole Polytechnique2020Master 2, Data Science
- Master 2 (M2), Multimedia NetworkingTélécom ParisTech2019Master 2 (M2), Multimedia Networking
Certifications
- AWS Machine Learning SpecialtyAmazon Web Services (AWS)2023