Job Summary

Generative AI & LLM Development
  Design and develop applications leveraging LLMs such as Gemini, and open-source models.
  Build Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge repositories.
  Develop AI-powered assistants, chatbots, copilots, and document intelligence solutions.
  Implement prompt engineering techniques to optimize model performance and output quality.
  Design and develop Agentic AI workflows for complex business processes.Solution Engineering
  Develop scalable APIs and microservices integrating AI capabilities.
  Build enterprise-grade applications using Python 
  Implement vector databases and semantic search solutions.
  Design orchestration workflows using LangChain, LangGraph, Semantic Kernel, AutoGen, or similar frameworks.

 

Required skills 
  Large Language Models (LLMs)
  Prompt Engineering
  Retrieval Augmented Generation (RAG)
  Agentic AI
  AI Assistants and Chatbots
  Fine-Tuning Concepts
  Vector Databases (BigQuery)

Key Responsibilities

1. To be responsible for providing technical guidance to a team of developers, enhancing their technical capabilities and increasing productivity.
2. To conduct comprehensive code reviews, establish and oversee quality assurance processes, performance optimization , implementation of best practices and coding standards to ensure successful delivery of complex projects.
3. To ensure process compliance in the assigned module| and participate in technical discussions/review as a technical consultant for feasibility study (technical alternatives, best packages, supporting architecture best practices, technical risks, breakdown into components, estimations).
4. To collaborate with stakeholders to define project scope, objectives, deliverables and accordingly prepare and submit status reports for minimizing exposure & closure of escalations.

Skill Requirements

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Other Requirements

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