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For Employers

ML Engineer

Incubit Global Business Service (GBS)

0 years

0 Lacs

india

Posted:12 hours ago| Platform: GlassDoor logo

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Skills Required

ml ai flow learning processing data engineering ocr algorithms software architecture python programming design documentation model code nosql rest github governance reinforcement retrieval networks

Work Mode

On-site

Job Type

Part Time

Job Description

AI/ML Engineer :

Location-Gurgaon

Work Mode- Hybrid (3 days WFO)

Notice Period : Immediate joiners

Experience : 4+yrs

AI/ML Job Description:

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

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ML Engineer