Lead Assistant Manager

Lead Assistant ManagerEXL/LAM/1443330

Digital SolutionsPune

Posted On

05 Aug 2025

End Date

19 Sep 2025

Required Experience

2 - 8 Years

Basic Section

Number Of Positions

1

Band

B2

Band Name

Lead Assistant Manager

Cost Code

G090154

Campus/Non Campus

NON CAMPUS

Employment Type

Permanent

Requisition Type

New

Max CTC

1200000.0000 - 2000000.0000

Complexity Level

Not Applicable

Work Type

Work From Home – Predominantly Working From Home But May Be Required To Work For A Certain No Of Days A Month From Office

Organisational

Group

EXL Digital

Sub Group

Digital Solutions

Organization

Digital Solutions

LOB

Digital Solutions

SBU

Xtrakto.AI

Country

India

City

Pune

Center

IN Pune C4

Skills

Skill

PYTHON

ARTIFICIAL INTELLIGENCE

Minimum Qualification

B.TECH/B.E

Certification

No data available

Job Description

EXL (NASDAQ: EXLS) is a $7 billion public-listed NASDAQ company and a rapidly expanding global digital data-led AI transformation solutions company with double digit growth. EXL Digital unit spearheads the development and implementation of Generative AI (GenAI) business solutions for our clients in Banking & Finance, Insurance, and Healthcare. EXL has partnered with NVIDIA AI Foundry

As a global leader in analytics, digital transformation, and AI innovation, EXL is committed to helping clients unlock the potential of generative AI to drive growth, efficiency, and innovation.

If you are a smart, self-motivated Machine Learning Scientist with a passion for advancing the field of Generative AI, we have an excellent opportunity for you. We are seeking candidates with deep expertise in developing and fine-tuning LLMs, RAG, agentic solutions, and knowledge graph technologies to drive innovative solutions in GenAI. You will be at the forefront of pioneering advancements in AI, working alongside some of the brightest minds in an exciting R&D environment to build cutting-edge capabilities that redefine the future of artificial intelligence.

• Develope initiatives in the GenAI domain, focusing on cutting-edge technologies like Large Language Models, Retrieval-Augmented Generation, and autonomous agents.
  
• Design and implement advanced workflows for integrating LLMs into real-world applications across various domains such as Finance, Insurance, and Healthcare.
  
• Develop and fine-tune domain-specific LLMs to optimize performance, using techniques like prompt engineering, adapter-based tuning, or low-rank adaptation.
  
• Drive the development of retrieval-augmented systems by combining LLMs with document retrieval, clustering, and search techniques.
  
• Stay at the forefront of AI advancements by reading, adapting, and implementing cutting-edge research to solve real-world challenges.
  
• Document research findings, methodologies, and implementations for internal and external stakeholders.
  

• Core Expertise:
  

+ Proven experience with Large Language Models (e.g., GPT-4, BERT, LLaMA, PaLM) and fine-tuning them for domain-specific applications.

+ In-depth knowledge of Retrieval-Augmented Generation workflows, including retrieval system design and document store integration.

+ Hands-on experience developing and deploying autonomous agents for complex problem-solving tasks.

• Tools & Frameworks:
  

+ Proficiency in deep learning frameworks like TensorFlow, PyTorch, or Hugging Face Transformers.

+ Experience with distributed training and optimization on GPUs and TPUs.

+ Familiarity with cloud ecosystems (AWS, Azure, Google Cloud) practices for scalable deployment.

• Research & Development:
  

+ Ability to read and adapt cutting-edge research papers for applied solutions in LLMs and knowledge graphs.

+ Expertise in domain adaptation, few-shot learning, and zero-shot reasoning.

+ Strong understanding of generative models, including VAEs, GANs, or diffusion models, and their integration with LLMs.

• Problem Solving:
  

+ Demonstrated ability to address challenges in unstructured data processing, including NLP and multimodal scenarios.

+ Experience with document retrieval, clustering, and unsupervised learning techniques.

• Experience with LLM fine-tuning and building Agentic systems for domain LLMs.
  
• Experience with reinforcement learning and fine-tuning via RLHF (Reinforcement Learning with Human Feedback).
  
• Knowledge of large-scale optimization methods for model training and inference.
  
• Familiarity with knowledge distillation and efficient model compression techniques.
  
• Strong collaboration and communication skills, with a proven ability to lead teams.
  
• Experience with MoE based architecture and knowledge on federated learning.
  

Workflow

Workflow Type

Digital Solution Center