As a Full Stack Engineer specializing in Agentic AI, you will bridge the gap between robust backend systems and cutting-edge AI reasoning. You will design and deploy scalable applications where AI agents interact with APIs, databases, and users in real-time.
Key Responsibilities
- Backend Development: Build and maintain scalable APIs and microservices using Node.js, TypeScript, and Python.
- AI Orchestration: Design and implement agentic workflows using frameworks like LangChain, CrewAI, or AutoGen.
- Frontend Integration: Develop intuitive, high-performance user interfaces using React or Next.js to visualize agentic reasoning and output.
- Tooling & Integration: Create tools (API connectors, scrapers, database interfaces) that allow AI agents to interact with the physical and digital world.
- RAG Implementation: Architect Retrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., Pinecone, Weaviate, or Milvus).
- Optimization: Fine-tune prompts and orchestration logic to reduce latency, prevent hallucinations, and optimize token usage.
- DevOps/MLOps: Deploy and monitor AI-driven applications, ensuring reliability and observability in production.
Required Skills & Qualifications
- Experience: 3+ years of professional experience in full-stack software development.
- Languages: Expert proficiency in TypeScript/JavaScript (Node.js) and Python.
- AI Knowledge: Proven experience working with LLMs (OpenAI, Anthropic, Open Source) and understanding of Agentic patterns (Reasoning/Planning, Tool-use, Memory).
- Frontend: Experience with modern frameworks like React.js and state management.
- Databases: Proficiency in SQL (PostgreSQL) and experience with Vector Databases.
- Infrastructure: Familiarity with Docker, cloud providers (AWS/GCP/Azure), and CI/CD pipelines.
- Problem Solving: A builder mindset—someone who can take a vague requirement and architect a multi-step AI solution.
Preferred Qualifications
- Experience with agentic frameworks.
- Contributions to open-source AI projects.
- Experience with stream processing and WebSockets for real-time AI updates.
- Understanding of Evals (evaluating AI performance/accuracy) and fine-tuning datasets.
