Generative AI Fullstack developer

Role & responsibilities

• 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 modern 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

General

• Experience with Git and collaborative development workflows
• Comfort working in fast-paced, ambiguous environments
• Strong problem-solving and communication skills

Nice to Have

• Certified Claude Architect – Foundations (CCA-F)
• Experience with Claude Code, Cursor, or other AI-assisted development tools
• Contributions to open-source AI projects
• Experience with multi-agent frameworks (AutoGen, CrewAI, LangGraph)
• Knowledge of MLOps practices and model deployment (MLflow, Weights & Biases)
• Familiarity with WebSockets for real-time AI streaming
• Experience with Kubernetes or serverless architectures