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.
ResponsibilitiesBuild 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 SkillsStrong 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 HaveWork across ML workflows: data ingestion, xysqume 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
ProfileComfortable owning work across backend, frontend, and ML integration
Able to move quickly between prototyping and production?grade implementation
Pragmatic, product?oriented, and comfortable operating in ambiguous environments
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