KPMG logo

Associate 1 / Analyst

KPMG

Hyderabad
Full-time
Experienced
42000 - 45000

Job Description

You will support the design, development, and implementation of generative AI models and systems. This involves understanding the problem domain, assisting in selecting appropriate models, helping in training them on large datasets, and fine-tuning hyperparameters under supervision

Required Skills

Strong Python programming skillsDeep Learning – LSTMStrong Expertise in AngularMachine Learning modelsExcellent communication skills

Key Responsibilities

  • Assist in optimizing generative AI algorithms to improve their efficiency, scalability, and computational performance
  • Evaluate the performance of generative AI models using appropriate metrics and validation techniques

Job Information

Job TypeFull-time
ExperienceExperienced
Experience Range1-2 years
Vacancies10
Work Location
Salarpuria Knowledge City, Orwell, 6th Floor, Unit-3, Survey No 83/1, Plot No 2, Raidurg, Hyderabad - 500081
Campus Recruitmentno
Monthly Salary₹42000 - ₹45000
Annual CTC6 LPA
Salary TypeFixed
Interview FormatIn-Person / Walk-In-Interview

Qualifications & Eligibility

Education Requirements

Graduate: B.Sc (IT/Computer Science)

Eligibility Criteria

English LevelFluent

About KPMG

Company Overview

Working at KPMG offers you a platform to grow with a team of diverse, passionate, highly skilled professionals. Grounded in a culture of trust, we're committed to creating a workplace that is equitable, inclusive and that prioritises the mental health and well-being

Company Details

IndustryOther
Company Size1000+
LocationHyderabad
Founded1987

Hiring Workflow

Know what to expect. Here is the typical process for this position.

Application

Submit your application to be considered for this role.

01

Virtual Interview

Face-to-face video interview with the hiring team.

Venue: Virtual — To be announced

02

Selection

Final evaluation and selection of qualified candidates.

03

Offer Release

Congratulations! Offer letter will be released to selected candidates.

04

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