Job Description

Senior Assistant Vice PresidentEXL/SAVP/1403930


Digital SolutionsNoida
Posted On
27 Jun 2025
End Date
11 Aug 2025
Required Experience
5 - 10 Years



Basic Section
Number Of Positions


1


Band


D2


Band Name


Senior Assistant Vice President


Cost Code


G090154


Campus/Non Campus


NON CAMPUS


Employment Type


Permanent


Requisition Type


New


Max CTC


6000000.0000 - 6500000.0000


Complexity Level


Not Applicable


Work Type


Hybrid - Working Partly From Home And Partly From Office


Organisational
Group


EXL Digital


Sub Group


Digital Solutions


Organization


Digital Solutions


LOB


Product Practice Market


SBU


GenAI R&D


Country


India


City


Noida


Center


Noida - Centre 59




Skills
Skill


NLP


AI


PYTHON


Minimum Qualification


B.TECH/B.E


Certification


No data available

Job Summary

As part of the EXL Digital AI Innovation team, you will work as a R&D in the Innovation unit supporting all EXL Business Units. You will be exposed to hundreds of different enterprise GenAI business use cases and help our clients create value architecting GenAI applications and solutions.


This role involves working on cutting-edge Generative AI technologies to drive significant revenue growth, operational efficiencies, and enhanced customer experiences for our Fortune 1000 enterprise clients across various industries in insurance, healthcare, banking & financial services, media, and retail.


You will work with a team of talented data scientists, AI researchers, software engineers, ML engineers, project managers and delivery managers helping them in deploying advanced AI technologies. You'll design AI systems & architectures at scale, improve AI Governance and responsible, LLM selection(GPT-4, Llama2, Mistral, Claude, Gemini, Flan, BERT) & fine-tuning PEFT LoRa/QLoRa adapters, RAG, LLM Prompt Engineering, Agentic AI, AutoGen, AI Autonomous Agents, Multimodal LLMs and LLM orchestration architectures for enterprise data at scale in Python.

Key Responsibilities

Experience of architecting AI systems to solve complex business problems. Build advanced RAG pipelines, text chunking, and retrieval, LLM Prompt Engineering, using Vector Databases. Implement right LLM selection based on use cases and client criteria (GPT-4, Llama2, Mistral, Claude, Gemini, Flan, BERT) while managing trifecta of accuracy, cost, and latency/scale. Develop, fine-tune, context tune and implement state-of-the-art NLP models including Large Language Models like GPT, Llama3.1, Claude, BLOOM, Flan-T5, Falcon etc. fine-tuning PEFT LoRa/QLoRa adapters

design AI systems & architectures, while considering good Responsible AI standards and AI Governance.

Build Agentic AI, AutoGen, Muti-agents use cases, AI Autonomous Agents and LLM orchestration architectures for enterprise data at scale in Python. Hands on experience with complementary technologies around LLMs like embedders, vector databases (chroma, weaviate etc.), orchestration tools like Langchain, LlamaIndex etc. Conduct research and experimentation to improve existing models and propose novel approaches. Collaborate with cross-functional teams to integrate generative AI solutions into real-world applications. Stay up-to-date with the latest advancements in deep learning and generative models and apply them to enhance our AI capabilities. Document research findings, prepare technical reports, and contribute to whitepaper/scientific publications. Provide deep leadership and coaching in the project delivery lifecycle. Focus on shared learning, continuous improvement, and drive adoption of best practices.

Qualifications

Education and Experience:

Masters of Science or PhD in computer science, data science, statistics, Natural Language Processing 2-3 years of experience in GenAI and Large Language Models. Hands-on experience with current deep learning frameworks (e.g., PyTorch, TensorFlow) as evidenced by released code (e.g., GitHub repositories - version control awareness). Solid understanding of optimization techniques for training deep neural networks, regularization methods, and hyperparameter/fine tuning. Experience in Generative AI Models and LLMs, finetuning LLMs, prompt engineering and experience with LLM orchestration frameworks like Langchain, LlamaIndex, RAGAS, etc. Strong software engineering skills for rapid and accurate development of AI models and systems. Understanding of Agentic AI, Autonomous Agents, AI Agents, AutoGen, Crew.ai, Langchain, and workflow steps design. NVIDIA Blueprints. Google AI Agents. Provide business-oriented solution with ability to communicate effectively, both verbally and in writing, with technical and non-technical stakeholders. Experience working in a collaborative environment, contributing to multidisciplinary teams and projects. Proven ability to solve complex problems, think creatively, and adapt to evolving research trends.

Skills and Competencies:

5 years of experience in AI, NLP including transformer architecture and LLMs, Computer Vision and related technologies Excellent communication and problem-solving skills. Working with cross-functional teams across different stakeholders. Ability to explain GenAI to non-technical audiences across many different industries. Ability to do GenAI Architecture solutioning for existing clients and potential clients. People leadership skills. Candidate is also hands on developer while people managing Data Scientists, ML Engineers etc. Experience in ML Engineering and MLOps, MLFlow Strong understanding of statistical and machine learning concepts Experience with deep learning frameworks such as TensorFlow and PyTorch Familiarity with key concepts and techniques used in generative models, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and flow-based models. Strong programming skills in languages such as Python, along with experience working with popular deep learning frameworks like PyTorch and TensorFlow. Understanding of Graph Database and/or Vector Database along with knowledge of cloud services (e.g., AWS, Azure, GCP). Experience with deploying AI models in production environments. Familiarity with domain-specific applications of generative AI Leveraged both Azure and AWS for model inferencing. For finetuning, he has worked more on AWS SageMaker. Has used different retrieval, reranking, and generation techniques based on the use case complexity and the metrics required. Has played multiple roles in the past, such as an individual contributor, a solution architect, a mentor, a trainer, and a people manager. He also participates in RFI, RFP, thought leadership, and business development activities.

Workflow
Workflow Type


Digital Solution Center