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Dicto AI

Open 32d

Full-Stack Engineer (AI/ML & MERN Stack)

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Summary

Builds and maintains full-stack applications using MERN stack (NestJS, React, TypeScript) while integrating AI/ML features like RAG systems, vector databases, and model-serving APIs for cloud-native deployments.

We’re looking for a full-stack engineer with strong NodeJS / Python experience, combined with solid MERN-stack development skills. This role mixes applied AI work, backend engineering, and full product delivery. You’ll work across data pipelines, APIs, model integration, retrieval-augmented generation (RAG) systems, and cloud-native deployments.



Le informazioni riportate di seguito illustrano i requisiti del ruolo, l'esperienza richiesta ai candidati e le qualifiche associate.
Responsibilities

  • Build and maintain full-stack applications using the MERN stack (NestJS, React, TypeScript).
  • Develop backend services and AI-driven features using TypeScript.
  • Help to maintain RAG pipelines, vector databases, embeddings workflows, and model-serving integrations.
  • Implement scalable APIs and microservices integrating ML or LLM-based components.
  • Deploy and manage services in AWS and/or GCP (compute, storage, networking, CI/CD).
  • Work with PostgreSQL, Redis and Qdrant for structured and unstructured data.
  • Collaborate with product and technical teams to take AI-powered features from prototype to production.
  • Maintain quality, performance, and reliability across the stack.

Required Skills

  • Strong TypeScript experience for backend development and applied ML.
  • Hands-on experience building RAG systems: vector stores, retrieval layers, embedding models.
  • Solid understanding of LLM integration, prompt patterns, and model-serving frameworks.
  • MERN-stack experience with strong React proficiency.
  • Strong Node.js and Express experience for API development.
  • Proficiency with PostgreSQL and database-schema design.
  • Experience deploying both traditional and ML workloads on AWS or GCP.
  • Good grasp of distributed systems, containers, and CI/CD workflows.

Nice to Have

  • Work across ML workflows: data ingestion, preprocessing, inference, and evaluation.
  • Experience with Haystack, or similar frameworks.
  • Exposure to GPU workflows, inference optimization, or fine-tuning.
  • Familiarity with serverless environments.
  • Experience with observability tools across backend and ML systems.

Profile

  • Comfortable owning work across backend, frontend, and ML integration.
  • Able to move quickly between prototyping and production-grade implementation. xysqume
  • Pragmatic, product-oriented, and comfortable operating in ambiguous environments.

Skills

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See also

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