Generative AI Systems Developer

10 hours ago


Mumbai, Maharashtra, India beBeeLargeLanguageModel Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

Job Summary:

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You will be a key member of the GenAI delivery organization and part of a GenAI project.

As a Generative AI Engineer, you will work with a team of other Turing engineers across different skill sets to develop and optimize LLM-based solutions.

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Required Skills & Qualifications:

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  • 5+ years of professional experience in building Machine Learning models & systems.
  • 1+ years of hands-on experience in how LLMs work & Generative AI (LLM) techniques particularly prompt engineering, RAG, and agents.
  • Expert proficiency in programming skills in Python, Langchain/Langgraph and SQL is a must.
  • Understanding of Cloud services, including Azure, GCP, or AWS.
  • Excellent communication skills to effectively collaborate with business SMEs.
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Responsibilities:

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  1. Develop and optimize LLM-based solutions : Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.
  2. Codebase ownership : Maintain high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.
  3. Cloud integration : Aide in deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.
  4. Cross-functional collaboration : Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.
  5. Mentoring and guidance : Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.
  6. Continuous innovation : Stay abreast of the latest advancements in LLM research and generative AI, proposing and experimenting with emerging techniques to drive ongoing improvements in model performance.
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