aimlroles

AI MLOPS/LLMOps Engineer

Fa Ewjt Saasfaprod1 Open · verified Oct 22, 2026
Hybrid Gurugram Full-time Mid-level ML Platform & Ops

At a glance

Mid-level ML Platform & Ops role at Fa Ewjt Saasfaprod1. Gurugram · full-time.

Pay not stated ML Platform & Ops salary

RoleML Platform & Ops
SeniorityMid-level
LocationGurugram
WorkplaceHybrid
EmploymentFull-time
PostedSep 22, 2026 · 4w ago
Role brief

aimlroles summary, based on the employer's posting.

What you'll do

  • Connect document classification, entity extraction, de-identification and trend-detection models to production workflows through Airflow on AWS EKS
  • Build retrieval pipelines that combine multilingual embeddings with lexical search in Aurora PostgreSQL
  • Parse nested JSON, email attachments and embedded PDFs from S3, then prepare multilingual text for search
  • Create batch workflows for entity refreshes, trend detection and de-identification, coordinating the named AWS services
  • Deploy and version models with MLflow and Databricks, manage database schema changes, and monitor retrieval quality

What you bring

  • You have hands-on experience putting AI or NLP models into production and building scalable data pipelines
  • You work confidently in Python and SQL, and have built workflows with Apache Airflow
  • You bring experience with AWS services and PostgreSQL or Aurora for data and document processing
  • You have used MLflow and Databricks for model deployment, monitoring or lifecycle management
  • You hold a bachelor's degree in a related field such as computer science, data science or mathematics

Who this fits

This mid-level, hybrid role is based in Gurugram. It suits someone who can build and operate multilingual document-processing and retrieval pipelines, and support models in production.

From the employer

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.

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
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.