AI Engineer

1 day ago

Pune, Maharashtra, India VITRIC Full time ₹ 20,00,000 - ₹ 25,00,000 per year

Job Description – AI Engineer

We are seeking an experienced AI Engineer with expertise in Python and prompt engineering. The ideal candidate will have a minimum of 2+ years of relevant experience, a good understanding of LLMs, LangGraph, LangChain, and AutoGen is a professional who designs, develops, and deploys intelligent systems utilizing large language models (LLMs) and advanced AI frameworks.

  • Python Proficiency:

Strong programming skills in Python are fundamental for developing and implementing AI solutions.

  • Prompt Engineering:

Expertise in crafting effective prompts to guide LLMs towards generating desired and accurate responses, often involving techniques like prompt chaining and optimization.

  • LLM Application Development:

Hands-on experience in building applications powered by various LLMs (e.g., GPT, LLaMA, Mistral). This includes understanding LLM architecture, memory management, and function/tool calling.

  • Agentic AI Frameworks:

Proficiency with frameworks designed for building AI agents and multi-agent systems, such as:

  • LangChain:
     A framework for developing applications powered by language models, enabling chaining of components and integration with various tools and data sources.
  • LangGraph:
     An extension of LangChain specifically designed for building stateful, multi-actor applications using LLMs, often visualized as a graph of interconnected nodes representing agents or logical steps.
  • AutoGen:
     A Microsoft framework that facilitates multi-agent collaboration, allowing specialized agents to work together to solve complex problems through task decomposition and recursive feedback loops.
  • Retrieval-Augmented Generation (RAG):

Experience in implementing and optimizing RAG pipelines, which combine LLMs with external knowledge bases (e.g., vector databases) to enhance generation with retrieved information.

  • Deployment and MLOps:

Practical knowledge of deploying AI models and agents into production environments, including containerization (Docker), orchestration (Kubernetes), cloud platforms (AWS, Azure, GCP), and CI/CD pipelines.

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