Full Stack AI Engineer

Location: Noida (On-site) Company: Zynence Labs Pvt. Ltd. Experience: 0–2 years Employment Type: Full-timeAbout Zynence Zynence is building an AI-native, full-lifecycle hardware engineering platform, a connected workspace that takes a hardware product from requirements and system architecture through BOM/cost, firmware, schematic/PCB, simulation, validation, and manufacturing. The platform runs on eleven purpose-built AI agents and a RAG-grounded knowledge layer, with a core architectural bet that generative AI handles drafting and structuring while deterministic engines compute anything an engineer would sign off on as a number, cost rollups, component derating, trace current capacity, battery runtime. We're targeting the sectors where design traceability is non-negotiable: medtech, aerospace & defense, robotics, EV hardware, and industrial IoT.Role Overview We're looking for an AI/ML Engineer who is equally comfortable building and shipping full-stack web features and building the AI/ML systems that power them. You'll work across our agent architecture and knowledge layer, from retrieval pipelines and LLM-powered agents to the web application and cloud infrastructure that put them in front of users. This is a hands-on, build-things role at an early-stage product company: you'll ship real features that go into a live platform, not a research sandbox.Key ResponsibilitiesAI/ML & Agent DevelopmentDesign, build, and iterate on LLM-powered agents within our multi-agent architecture (AGT-001–AGT-011).Build and improve retrieval-augmented generation (RAG) pipelines for our knowledge layer (AskZynence), including embedding generation, vector search, and retrieval quality tuning.Work within our generative/deterministic split — know when a task belongs to a language model and when it belongs to a deterministic engine, and build accordingly.Evaluate and integrate LLM APIs (OpenAI, Anthropic, or similar), including prompt design, structured output handling, and cost/latency tradeoffs.Contribute to data pipelines and evaluation frameworks that measure agent output quality over time.Full-Stack Web DevelopmentBuild and ship features across the frontend (React) and backend (Node.js/Express or equivalent) of the Zynence platform.Design and consume REST APIs; work comfortably with relational databases (PostgreSQL) and, where relevant, vector-enabled extensions (pgvector).Write clean, maintainable, well-tested code that other engineers can build on.Collaborate with the founders and engineering team to translate product requirements into working, production-ready features.Deployment & Cloud InfrastructureDeploy and maintain services on a cloud platform (AWS preferred; GCP/Azure experience also valued) — compute, storage, managed databases, and networking basics.Set up and maintain CI/CD pipelines for reliable, repeatable deployments.Use containerization (Docker) and basic orchestration where appropriate.Monitor application health and troubleshoot production issues across the stack, from infrastructure to application code to model behavior.QualificationsRequiredB.Tech/B.E. in Computer Science, IT, or a related field (or equivalent practical experience).0–2 years of hands-on experience building software - internships, personal projects, or professional work all count.Working proficiency in a MERN-equivalent stack: React on the frontend; Node.js/Express (or a comparable backend framework) and a relational or document database on the backend.Practical exposure to AI/ML concepts, this can be coursework, personal projects, hackathons, or professional experience: working with LLM APIs, embeddings, vector search, or classical ML.Comfort with Python, since most of our ML/agent tooling is Python-based even where the web app is JavaScript/TypeScript.Some exposure to deploying an application to the cloud (AWS, GCP, or Azure) — you don't need to be a DevOps expert, but you should have shipped something beyond localhost.Strong fundamentals in data structures, algorithms, and API design.PreferredHands-on experience with RAG pipelines, vector databases (pgvector, Pinecone, Weaviate, or similar), or LLM agent frameworks (LangChain, LlamaIndex, or custom agent architectures).Experience with prompt engineering and structured LLM output (function calling, JSON mode, or similar).Exposure to Docker and CI/CD tooling (GitHub Actions or similar).Interest in or exposure to hardware/engineering domains (electronics, embedded systems, PCB design, or manufacturing) — not required, but a genuine plus given what we're building.Experience working in a small team or startup environment where you own a feature end-to-end.What We Look ForEngineers who can move between "build the ML pipeline" and "ship the UI for it" in the same week.Strong ownership — you follow a feature from idea to production, not just to a pull request.Curiosity about both AI/ML and full-stack engineering — genuine interest in both sides of this role, not just one.Comfort with ambiguity: this is an early-stage product, and priorities will shift as we learn from real users.Clear communication — you can explain a technical tradeoff to a non-technical founder and a design decision to another engineer.Why Join Zynence?Build core AI/agent infrastructure for a category-defining product from an early stage, your work will directly shape the platform's architecture, not just extend it.Work across the full stack: AI/ML, backend, frontend, and cloud infrastructure, rather than being siloed into one layer.Direct collaboration with the founders on product and technical decisions.Real ownership on a live, filed-IP platform, not a side project or a research exercise.Fast learning curve across modern AI engineering (agents, RAG, LLM integration) and production web/cloud engineering.How to Apply Apply through LinkedIn and send your resume, along with links to any relevant projects (GitHub, deployed apps, or write-ups), to career@zynence.com Subject: Application for Full Stack AI Engineer – Zynence