Gen AI Engineer

Axtria; Hyderabad; On-site; INR 1,800,000 - 3,500,000; Full time

Job summary

Axtria is seeking experts to design and deploy Python-based applications on Azure, leveraging LLMs and Generative AI concepts. The role focuses on building scalable AI systems, model fine-tuning, and cross‑functional collaboration to integrate AI into products.

Candidates should be proficient in Python, SQL, and web technologies, with hands-on experience in Databricks Genie being a plus. You will contribute to end‑to‑end AI solution delivery in a dynamic environment.

Qualifications

• 3-12 years of experience developing, testing, and deploying Python-based apps on Azure platforms.
• Basic knowledge of Generative AI / LLMs / GPT concepts.
• Deep understanding of architecture and hands-on experience with Web Technologies.

Responsibilities

• Design and develop LLM-based systems.
• Build and deploy Generative AI solutions using Langchain and RAG techniques.
• Model development, fine-tuning, and refining large language models to meet business needs.
• Collaborate with cross-functional teams to integrate LLMs into broader apps and products.
• Design and implement effective prompts to guide the LLM for accurate outputs.

Skills

Python

SQL

Generative AI

Azure

Web Technologies

Tools

Databricks Genie

Job description

Qualifications

• 3-12 years of experience to develop, test, and deploy Python‑based applications on Azure platforms.
• Basic knowledge of Generative AI / LLMs / GPT concepts.
• Deep understanding of architecture and hands‑on experience in Web Technologies.
• Hands‑on experience with Python and SQL.
• Good to have Databricks Genie experience.

Responsibilities

• Design and develop LLM‑based systems.
• Build and deploy Generative AI solutions, often using frameworks like Langchain and techniques such as Retrieval‑Augmented Generation (RAG).
• Model development and fine‑tuning: train, fine‑tune, and refine large language models to meet specific business requirements or improve performance on particular tasks.
• Integration: collaborate with cross‑functional teams to integrate LLM models into broader applications and products.
• Prompt engineering: develop and implement effective prompts to guide the LLM and improve the accuracy and relevance of its outputs.