About This Role 4 to 6 years of experience
Skills: AI Generative Full Stack Developer
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 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