Full Stack Engineer — Agentic AI

JOB DESCRIPTION Full Stack Engineer — Agentic AI Department: Engineering / AI Products Location: Hybrid / Remote Experience: 5+ Years React / Next.js Node.js / Python Agentic Workflows Cloud & DevOps API Design ABOUT THE ROLE We are seeking a Full Stack Engineer with 5+ years of experience to design and deliver end-to-end AI powered applications, with a specific focus on deploying and operationalizing Agentic Workflows in production. You will bridge the gap between front-end user experiences and backend AI systems — building the interfaces, APIs, data pipelines, and orchestration layers that bring autonomous AI agents to life for enterprise users. KEY RESPONSIBILITIES • Design, build, and deploy full-stack web applications that expose agentic AI capabilities to end users via intuitive interfaces. Frontend Development • Develop responsive, accessible UIs using React, Next.js, or Vue.js that visualize agent reasoning steps, tool usage, and streaming outputs. • Implement real-time UI patterns (WebSockets, Server-Sent Events) to surface live agent progress and intermediate results to users. • Build agent configuration and prompt management consoles for business users to interact with and tune agentic systems. • Design component libraries and design systems that support multi-agent interaction patterns, chat interfaces, and workflow dashboards. Backend & Agentic Workflow Engineering • Build and deploy agentic workflow orchestration layers using LangGraph, AutoGen, CrewAI, or custom orchestration frameworks. • Design and implement RESTful and GraphQL APIs that expose agent capabilities to front-end applications and third-party integrations. • Develop backend services in Node.js and/or Python to manage agent state, session memory, and tool call execution pipelines. • Implement human-in-the-loop (HITL) approval workflows, escalation triggers, and audit trails within agentic pipelines. • Build and manage MCP (Model Context Protocol) server integrations to expose databases, APIs, and enterprise data as agent tools. • Integrate vector databases (Pinecone, Weaviate, pgvector) and knowledge graph layers to support RAG-grounded agent responses. Deployment & DevOps • Deploy agentic systems on cloud platforms (AWS / GCP / Azure) using containerized services (Docker, Kubernetes, ECS/GKE). • Build CI/CD pipelines for full-stack applications and agent workflows with automated testing, staging gates, and rollback support. • Implement observability stacks (logging, tracing, metrics) for both frontend interactions and backend agent execution chains. • Manage infrastructure as code (Terraform, Pulumi) for repeatable, auditable agentic system deployments. Security, Governance & Quality • Apply security best practices for AI-powered applications: API authentication, rate limiting, prompt injection defense, and output validation. • Implement role-based access control (RBAC) and audit logging for agentic workflows in regulated or enterprise environments. • Write comprehensive unit, integration, and end-to-end tests for both UI components and agentic backend services. REQUIRED SKILLS & EXPERIENCE • 5+ years of full-stack engineering experience delivering production applications. • Proven experience deploying at least one agentic AI workflow or LLM-powered application to production. • Strong proficiency in React or Next.js (frontend) and Node.js and/or Python (backend). • Experience with RESTful API design, GraphQL, and microservices architecture. • Familiarity with at least one agent orchestration framework: LangChain/LangGraph, AutoGen, CrewAI, or Semantic Kernel. • Hands-on experience with SQL and NoSQL databases; understanding of vector database concepts for RAG pipelines. • Solid understanding of streaming data patterns for real-time AI output rendering (SSE, WebSockets). • Experience with containerization (Docker) and deployment on major cloud platforms (AWS / GCP / Azure). • Proficiency with Git-based workflows, code reviews, and trunk-based development practices. • Strong communication skills to collaborate across data science, AI engineering, product, and business stakeholder teams. NICE TO HAVE • Experience with Kubernetes, Helm, or GitOps-based deployment patterns for scalable agent infrastructure. • Familiarity with MCP (Model Context Protocol) server design and tool-use integration patterns. • Exposure to LLM evaluation frameworks (RAGAS, Promptfoo, or LLM-as-judge) and agent testing strategies. • Experience with data lineage, metadata management, or enterprise data catalog platforms. • Knowledge of fine-tuning workflows and model serving (vLLM, TGI, Ollama) for self-hosted LLMs. • Contributions to open-source AI tooling or developer experience projects. WHAT WE OFFER • Opportunity to work at the frontier of applied Agentic AI — building real enterprise products, not prototypes. • Cross-functional teams combining AI researchers, data engineers, and product designers. • Competitive salary, flexible working arrangements, and access to top-tier AI tooling and infrastructure. • Continuous learning culture with investment in training, certifications, and conference participation.