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.