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AI Lead Engineer (RARR Job 6075)

For Digital Engineering And Cloud Transformation Company
8 - 12 Years
Full Time
Immediate
Up to 35 LPA
1 Position(s)
Gurugram
Posted 7 Days Ago

Job Skills

Job Description

Key responsibilities include:

  • Lead the design and development of AI native applications using tools like GitHub Copilot, Augment Code, and other AI-assisted development tools.
  • Experienced in leveraging cloud AI services and LLMs, like Azure AI services, Azure OpenAI, Vector Stores etc.
  •  Able to develop and manage AI Agents and Agentic AI workflows using LangChain, LangGraph, and other leading platforms.
  • Design and develop MCP servers to orchestrate multi-agent collaboration and task execution.
  • Collaborate with product and engineering teams to translate business requirements into scalable AI-powered solutions.
  • Guide the team in integrating AI capabilities into business applications, ensuring performance, security, and maintainability.
  • Mentor junior developers and foster a culture of innovation and continuous learning.
  • Stay current with emerging trends in AI development, including newer LLM Context techniques and Agentic AI.

Requirements:

  • Bachelor's or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field
  • 8 years of experience with a minimum of 2+ years of cumulative experience in the field, demonstrating a strong foundation in AI technologies.
  • Experienced in designing and developing enterprise-grade applications.
  • Strong proficiency in ReactJS, NodeJS, Python, and experience with machine learning.
  • Excellent problem-solving skills and ability to work effectively in a fast-paced environment.
  • Strong communication and teamwork skills, with the ability to collaborate effectively with cross-functional teams.
     

Nice to have:

  • Experience with fine-tuning machine learning models using frameworks like TensorFlow, PyTorch, AutoTrain, ChromaDB, and LoRA Weights.
  • Experience with deploying AI applications & models in cloud environments (e.g., AWS, Azure) and optimizing their performance for production use.
  • Researching and experimenting with different Generative AI techniques and incorporating them into solution offerings