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EXL
EXL
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EXL is a business consulting and services company that helps organizations use data, analytics, and technology to improve operations and make better decisions. Its work includes operations management, decision analytics, and digital transformation, with experience supporting clients in healthcare, insurance, finance, and logistics. EXL combines data science with practical business expertise to develop solutions tailored to each organization’s goals, operating model, and industry context.
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AI MLOps and LLMOps Engineer

Operationalize multilingual NLP and LLM capabilities in production AI systems. Build AWS data pipelines, hybrid search, model deployment workflows, and large-scale document processing solutions.

INIndiaHybridFull-timeMiddleSeniorEnglish RequiredMachine Learning Engineer
AirflowApacheAWSAzureCloudPostgresPythonSQL

Description

  • Integrate and refine inference pipelines for document classification, entity extraction, de-identification, and LLM-based trend detection.
  • Connect data science modules to end-to-end production workflows through Airflow DAGs running on AWS EKS.
  • Develop and optimize hybrid search using GTE multilingual dense embeddings and GIN lexical indexes on Aurora PostgreSQL.
  • Integrate an OpenAI-based API platform for multilingual query expansion and LLM-powered trend detection.
  • Design and maintain preprocessing pipelines for nested JSON, email attachments, and embedded PDFs stored in S3 and DataLake systems.
  • Process multilingual unstructured text spanning 300 GB of claim notes and documents.
  • Develop chunking methods and metadata extraction processes for embedding and retrieval workflows.
  • Create and maintain Airflow DAGs for monthly entity refreshes, trend detection, and de-identification batch processing.
  • Orchestrate data movement across AWS S3, Athena, Glue, Fargate, SQS, and Step Functions.
  • Scale processing systems for more than 500,000 claims and hundreds of millions of text chunks.
  • Deploy and version machine learning models with MLflow and Databricks.
  • Manage schema changes and database migrations with Liquibase on Aurora PostgreSQL.
  • Add logging, monitoring, and retrieval-quality evaluation scoring to production pipelines.

Requirements

  • Bachelor’s degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related discipline.
  • A master’s degree in Data Science, AI/ML, Computer Science, or Analytics is preferred but not required.
  • Cloud or data engineering certifications are advantageous.
  • Strong proficiency in Python as the primary language and SQL.
  • Experience integrating LLM APIs and working with multilingual embeddings, hybrid search, text classification, entity extraction, named entity recognition, and PII masking.
  • Experience with Apache Airflow, batch orchestration, and large-scale unstructured data processing.
  • Hands-on experience with AWS services including S3, Athena, Glue, Fargate, EKS, SQS, and Step Functions.
  • Knowledge of PostgreSQL or Aurora, pgvector, GIN indexes, and full-text search.
  • Experience using MLflow and Databricks or Azure Databricks.
  • Experience with GitHub-based development, CI/CD, schema management, and production support.

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