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Senior Backend Engineer builds AI-powered grant discovery and application tools using LLMs, tool-using agents, and RAG pipelines in Python, shipping production-grade services with robust evaluation and observability.
Build and scale production-grade LLM applications and AI systems for enterprises, owning architecture, data pipelines, deployment, and MLOps with Python, PyTorch/TensorFlow, and cloud-native tools.
Lead a backend and data engineering team to build a zero-hallucination RAG platform, designing scalable data pipelines, APIs, and distributed workflows using Python, Apache Beam, FastAPI, and Temporal.
Build and deploy production-grade LLM applications and agentic systems for enterprise clients using Python, PyTorch/TensorFlow, and MLOps tooling.
Build and deploy production-grade agentic AI systems using Python/Java and React, orchestrating multi-agent workflows and retrieval pipelines while ensuring reliability and observability.
Design and build LLM-powered multi-agent systems for digital banking using Google ADK and LangGraph, integrating RAG pipelines, tool calling, and robust orchestration for production deployment.
Designs modular AI components and orchestrates agentic workflows on Azure AKS, moving GenAI prototypes into scalable enterprise production.
Build and deploy multi-step LLM agents and RAG pipelines on AWS to automate insurance processes, using LangChain, LangGraph, and Langfuse for observability.
Build and deploy multi-step LLM agents and RAG pipelines on AWS to automate insurance workflows, using LangChain, LangGraph, and Langfuse for observability.
Builds and deploys generative-AI solutions (RAG, prompt engineering, AI guardrails) using LangChain, LangGraph, and PyTorch for enterprise clients in Milan.
Lead AI engineering teams to design and deliver scalable generative-AI and data platforms for clients in automotive, healthcare, and other sectors, using Python, cloud stacks, and MLOps tooling.
Full Stack Engineer building Python-based microservices with React frontends, deploying via Docker and CI/CD, and occasionally on-site at client locations in Italy.
Build and deploy Generative AI systems using LLMs, RAG pipelines, and cloud infrastructure; work with Python, LangChain/LangGraph, vector DBs, and SQL.
Build and deploy production-grade AI systems for enterprise clients, focusing on RAG, multi-agent architectures, and scalable GenAI pipelines in Python and cloud environments.
Builds and maintains backend services and APIs that integrate AI/LLM models and third-party systems, using Python/Java and cloud-native tools.
Build and maintain a universal data platform that integrates enterprise data sources into automated pipelines, ensuring high-quality data for AI agents, LLMs, and RAG workflows using Python, SQL, and GCP services.
Senior QA Engineer designs and automates tests for AI/LLM systems on Azure, building Python frameworks and CI/CD pipelines to validate reliability, correctness, and safety of GenAI deployments.
Build and maintain Java-based backend services using Spring Boot and microservices, designing REST APIs and integrating AI models.
Builds and maintains Java-based backend services using Spring Boot, REST APIs, and microservices for enterprise clients.
Design and build enterprise-grade AI systems using RAG, agentic workflows, and cloud platforms for HR and finance domains.
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