AI Engineer

EXL

. Manage GenAI model production pipelines, debug defects, and provide root cause assessments .

Posted 9/23/2026full-timePune • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in managing GenAI model production pipelines and implementing Retrieval-Augmented Generation architectures. Proficient in building LLM-based workflows and collaborating with cross-functional teams to define data requirements and success metrics.

Highest-signal resume keywords

GenAI Model Production ManagementLLM-Based Workflow DevelopmentRetrieval-Augmented Generation (RAG)Machine Learning Frameworks (scikit-learn, TensorFlow, PyTorch)Prompt Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceApplied Machine LearningGenAI FrameworksLangChainVector DatabasesPineconeDocument IngestionData ExtractionStatistical AnalysisPortfolio Development

Soft Skills

CollaborationCommunicationMentoringProblem SolvingContinuous Learning

Tools & Technologies

AWS ExtractVisualizationsReportsPresentationsAnalytics Tools

Industry Keywords

Generative AILarge Language Models (LLMs)Data RequirementsEvaluation MetricsAgentic AI

Tech Stack

Tools & technologies

AWSPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Manage GenAI model production pipelines, debug defects, and provide root cause assessments
Build and orchestrate LLM-based workflows using frameworks such as LangChain, including prompt engineering and pipeline design
Implement Retrieval-Augmented Generation (RAG) architectures using vector databases such as Pinecone for semantic search and contextual retrieval
Work with document ingestion and extraction workflows for unstructured documents, including PDFs, scans, and forms, using tools like AWS Extract and GenAI-based extraction techniques
Collaborate with engineering, product, and business stakeholders to define data requirements, evaluation metrics, and success criteria
Communicate findings and insights to technical and non-technical audiences through visualizations, reports, and presentations
Stay current with industry trends, tools, and best practices in Generative AI, LLMs, data science, and analytics
Mentor junior team members and contribute to continuous learning and technical excellence

Requirements

What you’ll need

Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, Statistics, Mathematics, or a related field
3–6 years of experience in a data science, applied ML, or GenAI role, with a strong portfolio of projects
Hands-on experience with machine learning frameworks: scikit-learn, TensorFlow, and PyTorch
Practical experience with LLMs, GenAI frameworks, LangChain, and prompt-driven workflows
Strong understanding of RAG patterns, vector embeddings, and vector databases such as Pinecone
Knowledge of Agentic AI preferred