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

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.

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
• Pragmatic, product‑oriented, and comfortable operating in ambiguous environments