Job Description

AI/ML Engineer :

Location-Gurgaon

Work Mode- Hybrid (3 days WFO)

Notice Period : Immediate joiners

Experience : 4+yrs

AI/ML :

Focus: Build & maintain AI/ Machine Learning/ GenAI models to solve business problems. Develop scripts to integrate those models into application flow. Maintain excellent documentation.

You will be part of the fast-paced team that enables AI/ML based solutions using ground-breaking technologies to deliver innovative solutions across multiple lines of business.

Key Skillsets : Machine Learning (ML) and Natural Language Processing (NLP), along with hands-on experience in Generative AI (GenAI) and a solid understanding of the LangChain framework.

Qualifications:

Bachelor's or higher degree in Data Science, Computer Science, Information Systems, Engineering, or related field Hands-on experience in building and deploying AI/ML models including Classical ML, Deep Learning, NLP, Generative AI, RAG, LangChain, OCR and Computer Vision. In-depth knowledge of machine learning algorithms, software architecture, libraries, and frameworks to create AI that accomplishes outlined goals Rigorous knowledge of Python programming, OOPs, and design patterns to develop software. Creative thinking and problem-solving skills to come up with new solutions and approaches Skillful in developing and maintaining detailed, easy-to-read documentation including data dictionaries, model documentation, and code documentation Experience in integrating AI/ML into existing applications in batch and RealTIME Experience with NoSQL is a plus Experience with REST APIs is a plus Experience with GitHub and leveraging CI/CD pipelines Experience in taking models through AI/ML governance in highly regulated industry is a plus

List of fundamental topics:

Machine Learning, Types of Machine Learning, Classification, Regression Deep Learning, Neural Networks LSTMs, GRUs, RNNs, transformers Natural Language Processing (NLP) Stemming, Lemmatization, Topic modelling Generative AI, Prompt engineering Reinforcement Learning from Human Feedback (RLHF) Retrieval-augmented generation (RAG) models Convolutional neural networks (CNNs) * YOLO, OCR