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 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)

- Robust 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

📌 AI Full Stack Developer (Chennai)
🏢 Bounteous
📍 Chennai