Lead Software Engineer

Virtusa; Minneapolis (MN); On-site; USD 110,000 - 150,000; Full time

Job summary

Virtusa seeks a data engineering professional to analyze customer domains and generate privacy-conscious synthetic conversation data for voice and chat AI deployments. You will build repeatable pipelines for data generation, ingestion, indexing, and evaluation across GCP and AWS, working with platform and DevOps teams to ensure high quality and retrieval accuracy.

The role emphasizes privacy safety, multilingual data generation, and exposure to large-language model assisted data work.

Qualifications

• Requires at least four years of experience in data engineering, conversation design operations, applied NLP data work, or knowledge-pipeline engineering.
• Experience with conversational platform data and retrieval-grounded knowledge preferred.

Responsibilities

• Analyze customer domains and generate realistic, privacy-safe synthetic conversation data for voice, chat, and conversational AI deployments.
• Build and maintain repeatable, parameterized data-generation, ingestion, indexing, and evaluation pipelines across GCP and AWS.
• Coordinate with platform and DevOps partners to ensure data pipelines meet privacy and quality standards.

Skills

Data Engineering

Synthetic Data Generation

RAG Pipelines

Knowledge Pipeline Engineering

Conversational AI

Applied NLP

Data Ingestion

Data Indexing

Retrieval Evaluation

Privacy and PII Validation

BigQuery

Amazon S3

Multilingual Data Generation

Quality Assurance

Job description

Analyze customer domains and generate realistic, privacy-safe synthetic conversation data and supporting knowledge content for voice, chat, and conversational AI deployments. Build and maintain repeatable, parameterized data-generation, ingestion, indexing, and evaluation pipelines across GCP and AWS, coordinating with platform and DevOps partners.

Requirements: Requires at least four years of experience in data engineering, conversation design operations, applied NLP data work, or knowledge-pipeline engineering, along with working knowledge of conversational platform data and retrieval-grounded knowledge. Candidates should demonstrate sound judgment in synthetic-data quality, retrieval quality, and privacy safety; LLM-assisted generation, BigQuery, S3, and multilingual data experience are preferred.

Key Skills: Data Engineering, Synthetic Data Generation, RAG Pipelines, Knowledge Pipeline Engineering, Conversational AI, Applied NLP, Data Ingestion, Data Indexing, Retrieval Evaluation, Privacy and PII Validation, BigQuery, Amazon S3, Multilingual Data Generation, Quality Assurance