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Dmitry D.DD

Dmitry D.

Senior AI / ML Engineer - RAG, LLMOps & AI Product

700 €/jour
Paris, FR
8-15 ans

Délai de réponse moyen : 1h

À propos de Dmitry

I help teams turn AI ideas into production systems: RAG assistants, agentic workflows, LLM evaluation, and ML products that survive latency, cost, and compliance constraints.

I am a senior ML / GenAI engineer and Data Science Lead with 8+ years across fintech, RegTech, document intelligence, and healthtech. I currently have my own French SASU, so I can work cleanly in a B2B setup and understand delivery accountability, client constraints, and the practical expectations of French/EU companies.

At DataSpike I lead a small ML team building compliance AI: RAG over regulatory/internal knowledge, multilingual adverse-media workflows across 10,000+ sources, NER/entity resolution, multimodal document verification, and LLMOps with prompt versioning, eval harnesses, monitoring, and provider fallback.

What I can own:
- GenAI / RAG strategy and MVP scoping for product teams
- LangChain / LangGraph agents, retrieval pipelines, vector and graph retrieval
- LLM evaluation, hallucination reduction, cost and latency optimization
- ML/NLP systems in Python, PyTorch, FastAPI, GCP/AWS, Docker and Kubernetes
- AI product discovery: framing use cases, writing testable specs, and working with business, compliance, and sales stakeholders

Good fit for senior ML engineering, GenAI lead, AI product owner/PM, or fractional AI advisor missions with clients in France, the EU, the UK, and remote US teams.
  • Anglais

    Capacité professionnelle complète

  • Français

    Capacité professionnelle limitée

  • Russe

    Bilingue ou natif

  • Serbe

    Notions

Accepte de travailler sur site
Paris (jusqu’à 30 km)

Expériences

  • DataSpike
    Data Science Lead - AI / GenAI
    BANQUE & ASSURANCES
    septembre 2022 - Aujourd'hui (3 ans et 9 mois)
    Lead an ML team building production AI features for a regulated KYC/AML SaaS product.

    Built and productionized RAG assistants and agentic workflows for compliance teams: retrieval over internal procedures and external sources, high-precision grounding, latency and token-cost constraints, and LangChain / LangGraph orchestration.

    Owned LLMOps for multilingual adverse-media analysis across 10,000+ sources in 40+ languages: NER/entity resolution, relevance scoring, deduplication, prompt/version management, evaluation harnesses, online monitoring, and provider fallback.

    Acted as technical referent for product, compliance, and sales teams, translating ambiguous business needs into testable AI specs and production delivery plans.
    MLOps LLM Machine learning Traitement automatique des langues (NLP) RAG
  • Quadcode trading platform
    Senior Data Scientist
    BANQUE & ASSURANCES
    mai 2021 - septembre 2022 (1 an et 4 mois)
    Built ML models for a fintech trading platform, with a focus on fraud detection, personalization, anomaly detection, and forecasting.

    Shipped a low-latency, interpretable event-based fraud detection model that reduced losses by about $1M/year.

    Built contextual bandits for deposit-offer personalization, improving average deposit in A/B testing, and developed anomaly-detection models for trading-platform KPIs.

    Improved implied-volatility forecasting with time-series models used directly by the trading product.
    Python Machine learning A/B Testing Développement et évaluation de modèles Data science
  • Tungsten Automation Kofax
    Software Engineer to Senior ML Engineer
    EDITION DE LOGICIELS
    août 2016 - mai 2021 (4 ans et 9 mois)
    Worked on enterprise document intelligence and OCR products, moving from backend engineering into applied ML.

    Built document-image understanding components, including paragraph detection for scanned documents and ML models supporting invoice-processing workflows.

    Used gradient boosting and production engineering practices to improve manual review efficiency and integrate ML components into enterprise automation products.

    This experience gave me a strong base in document AI, OCR, backend systems, and production constraints that I now apply to RAG and GenAI systems.
    Python Deep Learning Développement de services Backend SQL

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Formations

  • MSc + PhD coursework, Computer Science
    Volgograd State Technical University
    2020
    Research focus: road-accident prediction from historical data; machine learning, data science, algorithms and software engineering.

Compétences

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