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AIML Engineer

AIML Engineer

Atyeti Incdombivli, India
4 days ago
Job description

Role Overview :

We are seeking a highly skilled AI Engineer to design and implement intelligent systems using agentic AI capable of autonomous decision-making and task execution. This role involves integrating advanced machine learning models with reasoning, planning, and interaction capabilities to build scalable, intelligent applications. The ideal candidate will have a strong foundation in LLMs , prompt engineering , and web development , with hands-on experience deploying AI-powered web applications.

Key Responsibilities :

  • Design and develop agentic AI systems with autonomous task execution capabilities.
  • Integrate Large Language Models (LLMs) with reasoning , planning , and interaction frameworks .
  • Implement Retrieval-Augmented Generation (RAG) architectures using vector databases .
  • Engineer effective prompts and workflows for LLM-based agents.
  • Utilize agentic libraries (e.g., LangChain, AutoGPT, CrewAI, etc.) to build modular and scalable AI agents.
  • Develop and deploy web applications that interface with AI systems.
  • Collaborate with cross-functional teams to define system requirements and deliver robust solutions.
  • Optimize performance, scalability, and reliability of AI-powered applications.

Must-Have Skills :

  • Programming : Proficiency in Python and relevant AI / ML libraries.
  • LLMs : Experience working with models like GPT, Claude, Mistral, or open-source equivalents.
  • Prompt Engineering : Ability to design and refine prompts for complex tasks.
  • Vector Databases : Hands-on experience with FAISS , Pinecone , Weaviate , or Chroma .
  • RAG Architecture : Deep understanding of retrieval-based generation pipelines.
  • Agentic Libraries : Familiarity with frameworks like LangChain , AutoGPT , CrewAI , or similar.
  • Web Development : Prior experience in frontend / backend development and web app deployment (e.g., using Flask, FastAPI, React, Docker).
  • Deployment : Experience deploying applications on cloud platforms (AWS, Azure, GCP) or containerized environments.
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