Job Description

Lead Assistant ManagerEXL/LAM/1466402


EXL Digital CommonNoida
Posted On
01 Sep 2025
End Date
16 Oct 2025
Required Experience
3 - 6 Years



Basic Section
Number Of Positions


1


Band


B2


Band Name


Lead Assistant Manager


Cost Code


G090628


Campus/Non Campus


NON CAMPUS


Employment Type


Permanent


Requisition Type


New


Max CTC


900000.0000 - 1500000.0000


Complexity Level


-


Work Type


Hybrid - Working Partly From Home And Partly From Office


Organisational
Group


EXL Digital


Sub Group


EXL Digital Common


Organization


EXL Digital Common


LOB


CX Transformation Capability


SBU


CX CoE


Country


India


City


Noida


Center


Noida - Centre 59




Skills
Skill


NLP


SENTIMENT ANALYSIS


COMPUTER VISION


SPEECH ANALYTICS


AWS


PYTHON 3


BIG DATA


SQL


Minimum Qualification


BTECH


Certification


No data available

: Data Annotator

Job Summary

We are seeking a detail-oriented and highly skilled

Data Annotator

to support the development of AI and Machine Learning (ML) models by preparing, labeling, and curating large-scale datasets. The ideal candidate will possess a strong understanding of annotation techniques, quality assurance for labeled data, and practical exposure to

cloud-based tools (with a strong emphasis on AWS SageMaker Ground Truth, GCP Data Labeling, and Azure ML Data Labeling)

. This role is pivotal in ensuring the integrity, scalability, and accuracy of the data pipelines that power advanced AI systems.


The Data Annotator will collaborate closely with Data Scientists, Machine Learning Engineers, Cloud Architects, and Product Teams to deliver high-quality labeled datasets optimized for supervised learning, natural language processing (NLP), computer vision, and speech recognition models.

Key Responsibilities

Data Annotation & Labeling

Perform

manual and semi-automated labeling

of datasets across multiple modalities including text, audio, images, and video. Create high-quality annotations for: +

Text/NLP

: Named Entity Recognition (NER), sentiment analysis, intent classification, part-of-speech tagging, conversation structuring, and chatbot training datasets.
+

Computer Vision

: Bounding boxes, polygons, segmentation masks, key points, object tracking in videos, and OCR annotation.
+

Speech/Audio

: Transcription, speaker diarization, phoneme tagging, emotion labeling, and acoustic event detection.
Conduct

multi-tier annotation validation

and apply inter-annotator agreement processes to ensure labeling accuracy.

AWS & Cloud-Based Annotation

Leverage

AWS SageMaker Ground Truth

for scalable data labeling workflows including automated data labeling with active learning. Implement

quality control (QC) mechanisms in SageMaker Ground Truth

such as audit labels, annotation consolidation, and annotation jobs monitoring. Integrate annotated datasets into

AWS S3

, ensuring optimal storage structures and lifecycle policies. Work with

AWS Glue, Athena, and QuickSight

for dataset validation, analysis, and reporting. Exposure to

GCP Data Labeling Services

and

Azure ML Data Labeling

tools for multi-cloud environments (good to have). Collaborate with Cloud Engineers to automate annotation workflows using

Lambda functions, Step Functions, and event-driven pipelines.

Data Management & Quality Assurance

Perform

data preprocessing

: cleaning, normalization, anonymization (especially for PII data), and augmentation. Apply

data quality checks

to maintain dataset balance, reduce bias, and enhance representativeness. Document annotation guidelines, taxonomy structures, and ontology mapping for consistent labeling practices. Ensure compliance with

security and privacy standards

(GDPR, HIPAA, SOC2, ISO 27001) while working with sensitive datasets.

Collaboration & Continuous Improvement

Collaborate with ML Engineers and Data Scientists to refine annotation requirements based on evolving model performance. Participate in regular

feedback loops

with AI developers to improve annotation accuracy and dataset utility. Contribute to the design of

annotation ontologies

and

label taxonomies

for domain-specific projects (e.g., healthcare, finance, retail, manufacturing). Stay updated on

emerging annotation tools, AI-assisted labeling platforms, and best practices.

Required Skills & Competencies

Core Skills

Proven expertise in

data annotation for AI/ML applications

across text, image, and speech datasets. Strong proficiency with

AWS Cloud services

, especially

SageMaker Ground Truth

, S3, and Glue. Familiarity with

annotation platforms and tools

(Labelbox, Supervisely, CVAT, Prodigy, Doccano). Knowledge of

Python/SQL scripting

for dataset preparation and automation. Basic understanding of

machine learning concepts

(classification, object detection, NLP pipelines). Familiarity with

big data tools

(Apache Spark, Databricks - nice to have).

Domain Knowledge

Text/NLP

: Language models, chatbot training, intent recognition.

Computer Vision

: Object detection, OCR, autonomous systems labeling.

Audio/Speech

: Transcription guidelines, phoneme labeling, acoustic datasets. Understanding of

industry datasets

(healthcare records, retail data, insurance documents, call center logs).

Cloud Expertise

AWS (Priority)

: SageMaker Ground Truth, S3, Glue, Athena, QuickSight, IAM for role-based access control.

GCP (Good to Have)

: Vertex AI, AutoML, Data Labeling.

Azure (Good to Have)

: Azure ML Data Labeling, Azure Blob Storage, Azure Cognitive Services.

Qualifications

Bachelor's degree in

Computer Science, Data Science, Information Technology

, or related field. 2-5 years of experience in

data annotation, data labeling, or dataset preparation

for AI/ML projects. Hands-on experience with

AWS annotation workflows

and

multi-modal datasets

. Certification in

AWS Machine Learning Specialty

or

AWS Data Analytics Specialty

(preferred). Exposure to annotation in

regulated industries

(healthcare, finance, retail, government projects) is a plus.

Performance Metrics

Annotation Quality

: Accuracy and consistency of labeled data.

Efficiency

: Volume of annotations completed within SLA.

Cloud Integration

: Seamless delivery of datasets into AWS pipelines.

Error Reduction

: Continuous improvement of data validation and annotation accuracy.

Collaboration

: Effective communication with Data Science and Cloud Engineering teams.

Growth Path

Senior Data Annotator / Annotation Lead

managing teams of annotators.

Data Quality Analyst

leading data validation and audit processes.

ML Data Engineer

transitioning into dataset pipeline development roles.

AI/ML Specialist on AWS

specializing in automation and scaling of annotation pipelines

Workflow
Workflow Type


Digital Solution Center