GenAI Software Engineer

1 week ago

Bhavnagar, Gujarat, India beBeeArtificialIntelligenceEngineer Full time ₹ 15,00,000 - ₹ 22,00,000
About the Role

Turing is looking for individuals with LLM experience to join their GenAI delivery organization and contribute to solving business problems for Fortune 500 customers. As a key member, you will work closely with a team of Turing engineers across various skill sets.

In the past, the Turing GenAI delivery organization has implemented industry-leading multi-agent LLM systems, RAG systems, and Open Source LLM deployments for major enterprises.

Required Skills and Qualifications
  • 4+ 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 from various cloud services such as Azure, GCP, or AWS for building GenAI applications
  • Excellent communication skills to effectively collaborate with business SMEs
Benefits and Opportunities

You will be responsible for developing and optimizing LLM-based solutions by leading the design, training, fine-tuning, and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.

Maintain high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.

Aide in deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.

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.

Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.

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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