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 :

- Claude API & Anthropic SDK proficiency Hands-on experience with the Messages API, tool use / function calling, system prompt design, and model selection trade-offs (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.