Job Description

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 Title:

R&D Lead

Location:

India

Employment Type:

Full-time

Department:

EXL AI Innovation

Reports To:

Salary Range:

About Us

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.

Job Responsibilities:

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.

Qualifications:

Experience:

2-5 years in AI/ML research and development, with at least 1-2 years focusing on GenAI, LLMs, or related fields.

Education:

Master's or PhD in Computer Science, AI, or a related field from a top-tier institution is highly preferred.

Required Skills:

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.

Preferred Skills:

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