{"id":1621207,"url":"https://alion.io/job/exl-ai-mlopsllmops-engineer","title":"AI MLOPS/LLMOps Engineer","company":{"id":38016,"name":"EXL","domain":"exlservice.com","url":"https://alion.io/company/exl","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":80,"open_postings":57,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-10-06T05:45:30Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":18500,"max_usd":51000,"period":"year","method":"role_country_seniority_unknown","sample_n":136},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Aurora","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Fargate","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"GitHub","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Liquibase","optional":false},{"name":"LLM","optional":false},{"name":"MLFlow","optional":false},{"name":"NER","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"pgvector","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Java","optional":true},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-10-01T06:49:23Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-07T02:14:14Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"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.\nKey strengths should include:\nProficiency 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.\nStrong knowledge of Apache Airflow for orchestrating end-to-end data pipelines and automating batch processing workflows.\nExperience working with AWS services including S3, Athena, Glue, Fargate, EKS, SQS, and Step Functions.\nCapability to design and maintain large-scale document processing systems handling complex JSON structures, embedded documents, and multilingual content.\nFamiliarity with vector search and retrieval systems, including embeddings, pgvector, PostgreSQL/Aurora, GIN indexes, and full-text search.\nExperience with ML lifecycle management using MLflow, Databricks/Azure Databricks, model deployment, monitoring, and evaluation frameworks.\nStrong DevOps practices including GitHub-based development, CI/CD pipelines, schema management, and production support.\nWhat You Will Do\nAI Module Integration & Inference Pipelines\nIntegrate and adjust inference pipelines for NLP modules including document classification, entity extraction, de-identification (DEID), and LLM-based early trend detection\nConnect DS-coded AI modules into end-to-end production workflows via Airflow DAGs on AWS EKS\nBuild and tune hybrid search pipelines combining GTE multilingual dense embeddings with GIN lexical search on Aurora PostgreSQL\nIntegrate with OpenAI-based API platform for multilingual query expansion and LLM-driven trend detection\nDocument Processing & Parsing\nDesign and maintain document preprocessing pipelines that parse deeply nested JSON structures (emails with attachments, embedded PDFs) from S3/DataLake\nHandle multilingual unstructured text (English, Spanish, Portuguese, German, Dutch, French, Italian) across 300 GB of claim notes and documents\nBuild chunking strategies and metadata extraction for downstream embedding and retrieval workflows\nData Pipeline Engineering\nAuthor and maintain Airflow DAGs for batch processing (monthly entity refresh, trend detection, DEID pipeline)\nManage data flow across AWS services: S3, Athena, Glue, Fargate, SQS, Step Functions\nScale pipelines to handle 500K+ claims and hundreds of millions of text chunks\nProduction Deployment & Quality\nDeploy and version models using MLflow and Databricks\nManage schema evolution and migrations using Liquibase on Aurora PostgreSQL\nInstrument pipelines with logging, monitoring, and evaluation scoring for retrieval quality\n\nArea\nSkills\n\n Languages\nPython (primary), SQL\n\n AI / NLP\nLLM API integration, multilingual embeddings (e.g., GTE), hybrid search, text classification, entity extraction, NER, PII masking\n\nData Pipelines\nApache Airflow, batch orchestration, large-scale unstructured data processing\n\nCloud & Infrastructure\nAWS (S3, Athena, Glue, Fargate, EKS, SQS, Step Functions)\n\nDatabases\nPostgreSQL / Aurora, pgvector, GIN indexes, full-text search\n\nML Platform\nMLflow, Databricks / Azure Databricks\n\nDevOps\nGitHub, CI/CD pipelines\n\nEducation\nBachelor's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related field.\nMaster's degree in Data Science, AI/ML, Computer Science, or Analytics is preferred but not mandatory.\nRelevant cloud or data engineering certifications are 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