AI MLOPS/LLMOps Engineer
EXL (company site, opens in a new tab)GurugramOn-siteDirect career pageEst. 18–35 LPA
Posted on 22 Sept
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Job description
Structured overview of role & requirements
About This Role
- Lead integration and optimization of AI/LLM inference pipelines for NLP tasks such as document classification, entity extraction, and de-identification within AWS EKS using Airflow.
- Design, develop, and maintain large-scale multilingual document processing and data pipelines handling 300+ GB data, with batch orchestration across AWS services including S3, Athena, Glue, Fargate, SQS, and Step Functions.
- Deploy, version, and monitor AI/ML models using MLflow and Databricks, manage schema evolution on Aurora PostgreSQL, and ensure production pipeline quality and scalability to 500K+ claims and hundreds of millions of text chunks.
Minimum Requirements
- Proficiency in Python and SQL with experience in deploying AI/NLP solutions such as classification, NER, PII masking, de-identification, hybrid search, and LLM API integrations.
- Hands-on experience with Apache Airflow for orchestrating batch data pipelines and familiarity with AWS cloud services including S3, Athena, Glue, Fargate, EKS, SQS, and Step Functions.
- Experience with large-scale document processing of multilingual unstructured data and managing databases with PostgreSQL/Aurora, pgvector, GIN indexes, and full-text search.
- Bachelor's degree in Computer Science, IT, Data Science, AI, Statistics, Mathematics, or related field. Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Strong background in operationalizing complex AI and NLP pipelines in a cloud environment with AWS and Kubernetes (EKS).
- Skilled at handling large-scale, multilingual unstructured data and building scalable document processing systems with complex JSON and embedded documents.
- Experienced in full ML lifecycle management including model deployment, monitoring, and schema migrations with emphasis on production readiness and data integrity.