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Full Stack AI Engineer (Agentic AI Orchestration)

Alphanome.AI

Vishakhapatnam, Andhra Pradesh, Indiamidonsite

  • anthropic
  • autogen
  • aws
  • azure
  • crewai
  • docker
  • gcp
  • langchain
  • milvus
  • next.js
  • node.js
  • openai
  • pinecone
  • postgresql
  • python
  • react
  • typescript
  • weaviate
  • websockets

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.
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