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AI MLOPS/LLMOps Engineer

exl Service.com (India) Private Limited
Gurugram, IND
Full-time
Hybrid
Discovered 3 weeks ago

Full Job Posting

Qualifications

  • Education
  • Bachelor's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related field.
  • Master's degree in Data Science, AI/ML, Computer Science, or Analytics is preferred but not mandatory.
  • Relevant cloud or data engineering certifications are advantageous.

What You Will Do

  • AI Module Integration & Inference Pipelines
  • Integrate and adjust inference pipelines for NLP modules including document classification, entity extraction, de-identification (DEID), and LLM-based early trend detection
  • Connect DS-coded AI modules into end-to-end production workflows via Airflow DAGs on AWS EKS
  • Build and tune hybrid search pipelines combining GTE multilingual dense embeddings with GIN lexical search on Aurora PostgreSQL
  • Integrate with OpenAI-based API platform for multilingual query expansion and LLM-driven trend detection
  • Document Processing & Parsing
  • Design and maintain document preprocessing pipelines that parse deeply nested JSON structures (emails with attachments, embedded PDFs) from S3/DataLake
  • Handle multilingual unstructured text (English, Spanish, Portuguese, German, Dutch, French, Italian) across 300 GB of claim notes and documents
  • Build chunking strategies and metadata extraction for downstream embedding and retrieval workflows
  • Data Pipeline Engineering
  • Author and maintain Airflow DAGs for batch processing (monthly entity refresh, trend detection, DEID pipeline)
  • Manage data flow across AWS services: S3, Athena, Glue, Fargate, SQS, Step Functions
  • Scale pipelines to handle 500K+ claims and hundreds of millions of text chunks
  • Production Deployment & Quality
  • Deploy and version models using MLflow and Databricks
  • Manage schema evolution and migrations using Liquibase on Aurora PostgreSQL
  • Instrument pipelines with logging, monitoring, and evaluation scoring for retrieval quality

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