Assistant Manager at EXL Talent Acquisition Team

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

14 Jun, 26

Salary

0.0

Posted On

16 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, NLP, Deep Learning, Machine Learning, LLMs, RAG, Prompt Engineering, Feature Engineering, Text Classification, Information Extraction, Summarization, Semantic Search, DevOps, Cloud Deployment, scikit-learn, PyTorch

Industry

Business Consulting and Services

Description

Design, develop, and deploy AI/NLP solutions to solve diverse business challenges—particularly in areas like text classification, information extraction, summarization, and semantic search Conduct exploratory data analysis and feature engineering Contribute to the development initiatives in the GenAI domain, focusing on cutting-edge technologies like Large Language Models, Retrieval-Augmented Generation, and autonomous agents. Validate and monitor solution quality using real-world feedback data Work closely with ML engineers and DevOps teams to operationalize models (on cloud and on-prem environments) Hands-on experience on deploying solutions to cloud-native AI platforms (AWS/Azure/GCP) Collaborate with clients and business stakeholders to scope and refine requirements, validate model behavior, and ensure successful deployment Explore and experiment with LLMs, prompt engineering, and retrieval-augmented generation (RAG) techniques for advanced use cases Contribute to building reusable components, best practices, and scalable frameworks for AI delivery Exeperience of development of retrieval-augmented systems by combining LLMs with document retrieval, clustering, and search techniques. Qualifications: 3–6 years of hands-on experience in data science, with a focus on NLP, deep learning, and machine learning applications Strong programming skills in Python; experience with relevant libraries such as scikit-learn, spaCy, NLTK, PyTorch, TensorFlow, or Hugging Face Proven experience in delivering NLP/LLM-based solutions Familiarity with cloud platforms (AWS, Azure, or GCP) and experience with deploying AI models to production Ability to handle end-to-end ownership of solutions, from POC to deployment Prior experience in consulting or client-facing data science roles is a plus Exposure to document databases (e.g., MongoDB), graph databases, or vector databases (e.g., FAISS, Pinecone) is a bonus EXL is the indispensable partner for leading businesses in data-led industries such as insurance, banking and financial services, healthcare, retail and logistics. We bring a unique combination of data, advanced analytics, digital technology and industry expertise to help our clients turn data into insights, streamline operations, improve customer experience, and transform their business. Our partnerships with clients are built on a foundation of collaboration – and we’ve been chosen as a partner by nine of the top ten leading US insurance companies, nine of the top 20 global banks, and six of the top ten US health care payers. We function as one team to make your goals our goals, whether that’s unlocking the value of generative AI or embedding analytics into workflows that reduce risk or power your growth. Clients choose EXL as their transformation partner for many reasons. Our geographic diversity make talent all over the world instantly accessible. Digital accelerators enable unmatched speed-to-value, letting you realize results fast. It’s our people that truly set us apart, though, including the 1,500 data scientists we have dedicated to our generative AI practice. And our more than twenty years of experience in delivering business services, garnering stellar client references, and maintaining a solid balance sheet are reassuring to our C-suite clients. Find out for yourself why clients, employees, and analysts think we’re some of the best in the business. Contact us to see how we can help you achieve your goals.

Responsibilities

The role involves designing, developing, and deploying AI/NLP solutions for business challenges such as text classification and information extraction, while contributing to GenAI initiatives using technologies like LLMs and RAG. Responsibilities also include validating solution quality, operationalizing models with ML/DevOps teams, and collaborating with stakeholders to refine requirements.