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About the Role We are looking for a talented and passionate AI Generative Full Stack Developer to join our growing engineering team. You will be responsible for designing, building, and deploying intelligent AI-powered applications combining cutting-edge generative AI capabilities with robust full stack development using Python and React. You will work at the intersection of AI research and product engineering, turning LLM capabilities into real-world, production-ready features. Key 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 Required Skills & Experience AI & Agentic Engineering - 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 AI / LLM - Hands-on experience with LLM APIs (OpenAI, Anthropic, Cohere, or similar) - Experience building agentic systems (tool use, memory, multi-step reasoning) - Familiarity with prompt engineering techniques (chain-of-thought, few-shot, RAG) - Understanding of fine-tuning and model evaluation concepts Backend (Python) - Strong proficiency in Python 3.x - Experience with FastAPI or Django / Flask - Working knowledge of SQL and NoSQL databases (PostgreSQL, MongoDB, Redis) - Familiarity with async programming and background task queues (Celery, RQ) - Experience with Docker and deploying to cloud platforms (AWS, GCP, or Azure) Frontend (React) - Strong proficiency in React.js and contemporary JavaScript (ES6+) - Experience with TypeScript - 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 .
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