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Build and maintain AI agentic backend systems using Python, LangChain/LangGraph, and vector databases to power autonomous workflows and RAG pipelines for business intelligence.
Build and maintain cloud-native data pipelines and vector search infrastructure on AWS, Terraform, and Pinecone to power AI-driven products with multi-tenant architectures.
Build and optimize GCP-based data pipelines and vector databases to power AI models like LLMs and RAG systems for Doctolib’s AI Medical Companion, supporting healthcare professionals across Europe.
Builds and maintains the backend services and AI pipelines for an enterprise-grade conversational AI agent using Python, FastAPI/Flask, LangChain, and Google Gemini Enterprise on GCP.
Design and build scalable GCP data pipelines and deploy agentic AI systems using Vertex AI, LangChain, and RAG for enterprise clients.
Build AI-powered retail automation in Clojure: outfit generators, vector search, and LLM workflows that serve 100M+ shoppers via APIs for fashion retailers.
Architect and scale backend systems for high-throughput LLM interactions, vector search, and multi-agent orchestration, integrating model providers and data stores into performant pipelines.
Builds Python backend APIs and orchestrates LLM workflows, focusing on retrieval/search pipelines and enterprise integrations.
Senior Java developer building a scalable data/search platform with Java backend, React/TypeScript frontend, and cloud-based search engines like Solr/OpenSearch.
Build and optimize scalable search infrastructure using Elasticsearch, Solr, and vector databases to power fast, intelligent user experiences in a SaaS platform.
Build and ship AI features into existing C# .NET ERP products, integrating LLMs via prompt design, RAG, and evaluation pipelines while ensuring production-grade reliability and accuracy.
Lead a data engineering team to build and scale secure, high-integrity pipelines and warehouses using Python, Airflow, Databricks, and AWS, while enforcing governance and quality standards.
Lead a team to design and build scalable data pipelines and AI-ready infrastructure for an AI startup, focusing on real-time processing, vector search, and ML data workflows.
Build secure, scalable data pipelines and AI-ready platforms for Defence customers using Python, Kafka, Spark, and Kubernetes in air-gapped environments.
Build and maintain scalable data pipelines and AI-ready datasets for analytics and agentic systems using cloud platforms like AWS/Azure/GCP and tools such as Databricks and Snowflake.
Design and deliver enterprise Snowflake data platforms, lead client-facing technical discussions, and optimize cloud data solutions for large-scale transformations.
Design and govern enterprise-scale AI-ready data platforms using Azure Databricks, Snowflake, and Delta Lake to enable analytics, GenAI, and ML workloads.
Lead AI/ML and Generative AI projects from concept to production, coordinating cross-functional teams and ensuring Responsible AI compliance while driving enterprise adoption and performance tracking.
Designs and governs enterprise-scale data platforms using Azure Databricks, Snowflake, and Lakehouse architecture to enable AI, analytics, and GenAI workloads.
Builds and improves search relevancy, ranking, and ML-powered enrichment systems for AWS Marketplace, impacting how millions find software using hybrid search and embeddings.
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