EXL

Analyzes business data and runs outsourced operations

AI MLOps/LLMOps Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeGurugram, Haryana, IndiaNo salary listedIn-person

About the Job

Requirements

Proficiency in Python and SQL and experience deploying AI/NLP solutions such as document classification, entity extraction, named entity recognition, personally identifiable information masking, de-identification, hybrid search, and large language model integrations.
Strong knowledge of Apache Airflow for orchestrating end-to-end data pipelines and automating batch processing workflows.
Experience with AWS services including S3, Athena, Glue, Fargate, EKS, SQS, and Step Functions.
Ability to design and maintain large-scale document processing systems handling complex JSON structures, embedded documents, and multilingual content.
Familiarity with vector search and retrieval systems, including embeddings, pgvector, PostgreSQL/Aurora, GIN indexes, and full-text search.
Experience with machine learning lifecycle management using MLflow, Databricks/Azure Databricks, model deployment, monitoring, and evaluation frameworks.
Strong DevOps practices including GitHub-based development, CI/CD pipelines, schema management, and production support.
Bachelor's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related field.

Responsibilities

Integrate and adjust inference pipelines for NLP modules including document classification, entity extraction, de-identification, and LLM-based early trend detection.
Connect data-science-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 an OpenAI-based API platform for multilingual query expansion and LLM-driven trend detection.
Design and maintain document preprocessing pipelines that parse deeply nested JSON structures, including emails with attachments and embedded PDFs, from S3/DataLake.
Process multilingual unstructured text across 300 GB of claim notes and documents.
Build chunking strategies and metadata extraction for downstream embedding and retrieval workflows.
Author and maintain Airflow DAGs for batch processing, including monthly entity refresh, trend detection, and de-identification pipelines.
Manage data flow across AWS services including S3, Athena, Glue, Fargate, SQS, and Step Functions.
Scale pipelines to handle 500K+ claims and hundreds of millions of text chunks.
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.

Desired Qualifications

A 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.

Seeking a strong Data Engineer / AI Engineer with expertise in building and operationalizing large-scale AI and NLP solutions on cloud platforms. The ideal candidate should have hands-on experience integrating AI/LLM models into production workflows, developing scalable data pipelines, and processing large volumes of multilingual unstructured text.

Key strengths should include:

Proficiency in Python and SQL with experience deploying AI/NLP solutions such as document classification, entity extraction, NER, PII masking, de-identification, hybrid search, and LLM integrations.
Strong knowledge of Apache Airflow for orchestrating end-to-end data pipelines and automating batch processing workflows.
Experience working with AWS services including S3, Athena, Glue, Fargate, EKS, SQS, and Step Functions.
Capability to design and maintain large-scale document processing systems handling complex JSON structures, embedded documents, and multilingual content.
Familiarity with vector search and retrieval systems, including embeddings, pgvector, PostgreSQL/Aurora, GIN indexes, and full-text search.
Experience with ML lifecycle management using MLflow, Databricks/Azure Databricks, model deployment, monitoring, and evaluation frameworks.
Strong DevOps practices including GitHub-based development, CI/CD pipelines, schema management, and production support.

Area	Skills
Languages	Python (primary), SQL
AI / NLP	LLM API integration, multilingual embeddings (e.g., GTE), hybrid search, text classification, entity extraction, NER, PII masking
Data Pipelines	Apache Airflow, batch orchestration, large-scale unstructured data processing
Cloud & Infrastructure	AWS (S3, Athena, Glue, Fargate, EKS, SQS, Step Functions)
Databases	PostgreSQL / Aurora, pgvector, GIN indexes, full-text search
ML Platform	MLflow, Databricks / Azure Databricks
DevOps	GitHub, CI/CD pipelines

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

About the Company

EXL

Analyzes business data and runs outsourced operations

EXL analyzes business data and runs outsourced operations for insurers, healthcare payers, banks, retailers, and other large organizations. Its teams combine industry expertise with data engineering and AI to handle tasks such as claims processing, payment reviews, customer service, and finance within existing technology environments. The company also builds software, including LifePRO, which manages insurance policies, and EXLdata.ai, which connects fragmented data for use in AI systems. Clients pay for consulting projects, ongoing managed services, and software delivered through the cloud. EXL was founded in 1999 and is headquartered in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1999

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