Engineering Manager, Data Science and Machine Learning

Role:
Engineering Manager, Data Science and Machine Learning

Location:

Vashi

Shift:  General

Role Overview

As an Engineering Manager, AI & ML (Data Collection), you will play a vital role in executing the company’s AI and machine learning initiatives, with a strong focus on document ingestion and enrichment technologies. This position requires deep technical expertise in unstructured data processing, data collection pipeline engineering, and a hands-on approach to managing and mentoring engineers.
Your leadership will ensure that AI & ML data collection systems are developed and operationalized to the highest standards of performance, reliability, and security. You will work closely with individual contributors to ensure projects align with broader business goals and AI/ML strategies.
This role requires deep engagement in the design, development, and maintenance of document enrichment AI & ML models, solutions, architecture, and services. You will provide strong technical direction, solve complex technical challenges, and ensure the team consistently delivers high-quality, scalable solutions. You will leverage your deep knowledge in document understanding and enrichment, advanced natural language processing (NLP), OCR, entity extraction and enrichment, duplicate detection, generative AI (GenAI), large language models (LLMs), ML Operations (MLOps), data architecture, data pipelines, and cloud-managed services.
Your leadership will ensure AI/ML systems align with global business strategies while maintaining seamless integration and high performance. You will oversee the end-to-end lifecycle of AI/ML data systems—from research and development through deployment and operationalization.
You will mentor team members, resolve technical challenges, and foster a culture of innovation and collaboration, ensuring teams have the tools, frameworks, and guidance needed to succeed.

This role offers a unique opportunity to drive impactful change in a fast-paced, dynamic environment, directly contributing to the success of global AI/ML initiatives.
Your ability to collaborate with cross-functional stakeholders, provide leadership across locations, set high standards, and hire, train, and retain exceptional talent will be foundational to your success. You will solicit feedback, engage others with empathy, inspire creative thinking, and help foster a culture of belonging, teamwork, and purpose.

Team Overview

You will lead a team of machine learning engineers responsible for building AI & ML solutions and services as part of robust document ingestion and enrichment pipelines handling large volumes of unstructured data. The team focuses on building scalable, reliable systems to process, enrich, and categorize data essential for downstream data collection and analytics.

Outline of Duties and Responsibilities

AI & ML Data Collection Leadership:  Drive the execution of AI & ML initiatives related to data collection, ensuring alignment with overall business goals and strategies.
Document Enrichment Ownership:  Own and evolve enrichment models for all incoming documents, including OCR, document structure extraction, entity extraction, entity resolution, and duplicate detection to ensure high-quality downstream data consumption.
Technical Oversight:  Provide hands-on technical leadership in the engineering of ML models and services, focusing on unstructured document processing, NLP, classifiers, and enrichment models. Oversee and contribute to scalable, reliable, and efficient solutions.
Team Leadership & Development:  Lead, mentor, and develop a high-performing team of engineers and data scientists. Foster a culture of innovation, continuous improvement, and effective communication across geographically dispersed teams.
NLP Technologies:  Contribute to the development and application of NLP techniques, including OCR post-processing, classifiers, transformers, LLMs, and other methodologies to process and enrich unstructured documents. Ensure seamless integration into the broader AI/ML infrastructure.
Data Pipeline Engineering:  Design, develop, and maintain advanced document ingestion and enrichment pipelines using orchestration,…