Overview

We are looking for a talented and passionate AI Generative Full Stack Developer to join our growing engineering team.

Responsibilities

• Design and develop AI-powered full stack applications using Python (backend) and React (frontend)
• Build and maintain agentic frameworks and LLM pipelines using tools like LangChain, LlamaIndex, or custom implementations
• Integrate generative AI APIs (OpenAI, Anthropic Claude, Gemini, etc.) into scalable web applications
• Develop RESTful and GraphQL APIs to connect AI backends with React frontends
• Implement RAG (Retrieval-Augmented Generation) systems using vector databases (Pinecone, Weaviate, ChromaDB)
• Build and optimize prompt engineering workflows and evaluation pipelines
• Collaborate with product, design, and data science teams to ship AI features end-to-end
• Write clean, testable, and well-documented code
• Monitor, debug, and optimize AI model performance in production
• Stay current with the rapidly evolving generative AI landscape

Qualifications

• Claude API & Anthropic SDK proficiency hands-on experience with the Messages API, tool use / function calling, system prompt design, and model selection tradeoffs (Sonnet vs. Opus vs. Haiku); familiarity with context window management and token budgeting
• Agentic loop architecture ability to design reliable multi-step agent loops: tool orchestration, retry logic, error recovery, and knowing when to stop or escalate rather than loop indefinitely
• Tool/MCP integration experience building and connecting tools (internal APIs, databases, external services) via Anthropic's tool use schema or MCP servers, including input validation and graceful failure handling
• Prompt engineering & evaluation skilled at structured prompting (system prompts, few-shot examples, XML tagging), and building prompt eval harnesses to measure output quality, regression-test changes, and tune instructions systematically
• Observability & auditability knows how to log full agent traces (inputs, tool calls, intermediate outputs, final responses) in a structured, queryable format; experience with tools like LangSmith, Braintrust, Helicone, or custom tracing pipelines
• Measurement & KPI design can define and instrument meaningful agent metrics: task completion rate, tool call accuracy, hallucination rate, latency per step, cost per run, and human-in-the-loop escalation rate; connects agent telemetry to business outcomes
• Human-in-the-loop & guardrails understands when to inject human review checkpoints, how to design approval gates for high-stakes actions, and how to implement input/output guardrails (content filtering, schema validation, confidence thresholds)
• Cost & latency optimization experience profiling and reducing inference costs through prompt caching, batching, streaming, and appropriate model tiering, without sacrificing reliability
• Security & data handling awareness of prompt injection risks, credential/secret hygiene in agentic contexts, PII handling, and least-privilege design when agents have access to real systems or external APIs
• Software engineering fundamentals strong async Python (or TypeScript), testing discipline (unit + integration tests for agent components), CI/CD, and the ability to decompose complex agent systems into maintainable, modular code
• Hands-on experience with LLM APIs (OpenAI, Anthropic, Cohere, or similar)
• Familiarity with prompt engineering techniques (chain-of-thought, few-shot, RAG)
• Understanding of fine-tuning and model evaluation concepts
• Strong proficiency in Python 3.x
• Working knowledge of SQL and NoSQL databases (PostgreSQL, MongoDB, Redis)
• Familiarity with async programming and background task queues (Celery, RQ)
• Strong proficiency in React.js and contemporary JavaScript (ES6+)
• Familiarity with state management (Redux, Zustand, or Context API)
• Ability to build streaming UI for LLM outputs (token-by-token rendering)
• Basic understanding of UX principles for AI interfaces .