Gen AI Engineer

Permanent job opening for Gen AI Engineer with MNC organization at Pune

WORK MODE :  Hybrid - ( Mon Fri )

( Shift Timings 6 pm 1 am )

Interview Rounds

Interview Process:

2-step interview - The first step is a screening round from the HR Team , followed by 2 rounds  interview with  technical panel

Please fill in the details mentioned below and share on  amishdelta@gmail,com

Alternate Email :
Date of Birth :

Reason for Job Change :

Immediate 15 days

Years of Experience  - 5 - 10 yrs

Required skills

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.

Experience in driving the engineering team toward a technical roadmap.

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

Roles & Responsibilities

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.

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.

Cloud integration: 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.