New opportunity

Lead Software Engineer

Virtusa · Singapore

About this role

The Lead Software Engineer role is a Python-focused Data Engineering position centered on building scalable data infrastructure to support AI and Generative AI applications. Responsibilities include developing reusable Python modules for data ingestion, performing exploratory data analysis (EDA), optimizing data workflows for performance and reliability, and building data services and APIs for downstream AI integration. The role requires ensuring data quality and observability across end-to-end data-to-AI pipelines. Key responsibilities include: Responsibilities: - Build and maintain scalable data pipelines and processing systems. - Develop reusable Python modules for data ingestion and automation. - Perform EDA to identify data patterns and quality issues. - Support data requirements for ML and Generative AI systems, including RAG and agent workflows. - Build APIs to support AI applications. Required Qualifications: - Strong hands-on experience with Python and data libraries: pandas, NumPy, Matplotlib, scikit-learn, and PyTorch. - Proficiency in SQL for data extraction and manipulation. - Experience with AI model integration (OpenAI, Anthropic, LLaMA, Mistral). - Familiarity with AI orchestration frameworks such as LangChain, AutoGen, and CrewAI. - Understanding of data modeling and analytics workflows. Preferred Qualifications: - Experience with big data or distributed processing systems. - Knowledge of vector databases and embedding-based retrieval systems. - Experience building APIs for data/AI systems. - Familiarity with cloud platforms (AWS, Azure, GCP). - Exposure to production monitoring and data observability tools.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonETLSQLPandasNumPyMatplotlibscikit-learnPyTorchOpenAIAnthropicLLaMAMistralLangChainAutoGenCrewAINoSQLOracleAWSGCPAPI Development

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Scalable Data Pipeline Engineering
The core of the role is building and maintaining robust, reusable Python modules for data ingestion and processing. You must demonstrate the ability to design systems that handle large datasets efficiently while ensuring reliability and performance for downstream AI applications.
AI and Generative AI Integration
You are expected to bridge the gap between raw data and AI models. This involves preparing datasets for LLM-based systems, specifically supporting RAG (Retrieval-Augmented Generation) and agent-based workflows using frameworks like LangChain, AutoGen, or CrewAI.
Data Quality and Observability
Ensuring the integrity of data-to-AI pipelines is critical. You must implement strategies for exploratory data analysis (EDA) to identify patterns and quality issues, while maintaining observability to monitor the health and consistency of data flowing into AI models.
API Development for AI Services
Beyond data processing, you must build data services and APIs that allow AI applications to consume processed information. This requires a strong grasp of API design to ensure seamless integration between the data infrastructure and external AI model providers.

Likely challenges to think through

Balancing Performance with AI Flexibility
A common challenge in AI-focused data engineering is optimizing pipelines for high-volume data while maintaining the flexibility required for experimental AI workflows. Prepare by discussing how you balance rigid data engineering best practices with the iterative, often unpredictable, needs of ML and LLM development.
Managing Multi-Model AI Orchestration
Integrating diverse AI providers (OpenAI, Anthropic, LLaMA, Mistral) requires managing different API behaviors and data requirements. Prepare by explaining how you design modular orchestration layers that prevent vendor lock-in and handle the varying latency and output formats of different LLMs.
Ensuring Data Quality in Unstructured Contexts
Generative AI often relies on unstructured data, which is harder to validate than traditional tabular data. Prepare to discuss your approach to data observability and quality assurance when dealing with the vector embeddings and unstructured text inputs typical of RAG systems.

Practice your answers

Technical practice

Designing Scalable Data Pipelines for AI

What to coverExplain your approach to building data pipelines that handle large-scale ingestion while maintaining low latency for downstream AI applications. Discuss how you ensure data quality and observability at each stage of the pipeline to support reliable Generative AI workflows.

Optimizing Python Data Workflows

What to coverDescribe techniques you use to optimize performance when processing large datasets using pandas and NumPy. Provide examples of how you identify bottlenecks in data ingestion or transformation modules and how you refactor code for better scalability.

Implementing RAG and Agent Workflows

What to coverHow do you structure data to support Retrieval-Augmented Generation (RAG) systems? Discuss the role of vector databases and embedding-based retrieval in these workflows and how you integrate them with orchestration frameworks like LangChain or AutoGen.

AI Model Integration and API Development

What to coverWhen building APIs to serve AI models (e.g., OpenAI, LLaMA, Mistral), what considerations do you prioritize for security, rate limiting, and error handling? Explain how you manage the integration of multiple model providers within a unified data service.

Exploratory Data Analysis (EDA) for AI Readiness

What to coverDescribe your process for performing EDA to detect data quality issues before feeding data into ML or Generative AI models. How do you use visualization tools like Matplotlib to communicate these findings to stakeholders?

SQL and Data Modeling for AI Systems

What to coverHow do you approach data modeling when the requirements involve both structured SQL data and unstructured data for AI? Discuss the trade-offs between different database types and how you ensure consistency across the end-to-end pipeline.

Production Monitoring and Observability

What to coverWhat strategies do you employ to monitor data pipelines in production? Explain how you implement observability to track data drift, pipeline failures, and model performance metrics.

Leveraging Deep Learning Libraries

What to coverDiscuss your experience using PyTorch for AI model experimentation. How do you prepare and structure datasets specifically for deep learning tasks compared to traditional machine learning workflows?

Behavioural practice

Handling Technical Trade-offs in Architecture

What to coverDescribe a situation where you had to choose between two different architectural approaches for a data pipeline. Explain the factors you considered, the trade-offs involved, and how you justified your final decision to the team.

Managing Data Quality Issues in Production

What to coverTell me about a time you discovered a significant data quality issue in a production pipeline. What steps did you take to identify the root cause, mitigate the impact on downstream AI systems, and prevent recurrence?

Collaborating on Cross-functional AI Projects

What to coverProvide an example of how you have collaborated with data scientists or AI researchers to translate their requirements into scalable data infrastructure. How did you ensure the data services you built met their specific needs?

Adapting to New AI Technologies

What to coverThe AI landscape evolves rapidly. Describe a time you had to quickly learn and implement a new framework or model provider (e.g., a new LLM or orchestration tool) to meet a project deadline. How did you manage the learning curve while maintaining delivery?

Sharpen your knowledge

Topics to prepare

Generative AI Orchestration and RAG
The role requires supporting RAG systems and agent workflows using frameworks like LangChain, AutoGen, or CrewAI. Review the conceptual architecture of Retrieval-Augmented Generation, specifically how data ingestion pipelines feed into vector databases to provide context for LLMs. Exercise: Design a high-level architecture diagram for a RAG pipeline that ingests raw documents, creates embeddings, and retrieves them for an LLM query.
Data Observability and Quality in AI Pipelines
Ensuring data quality and observability is a core responsibility for end-to-end AI pipelines. Focus on the concepts of data lineage, schema validation, and monitoring for drift in production data. Exercise: Define a set of metrics or checks you would implement to monitor the health and quality of a data pipeline feeding a production-grade generative AI model.

Skills to revise

Python Data Engineering Libraries
The role mandates hands-on proficiency with pandas, NumPy, and scikit-learn for data manipulation and analysis. Exercise: Create a Python script that performs an automated EDA on a sample dataset, including identifying missing values, calculating descriptive statistics, and visualizing key patterns using Matplotlib.
API Development for AI Integration
You will be building data services and APIs to support downstream AI applications. Exercise: Develop a simple RESTful API using a framework like FastAPI that accepts a user query, retrieves relevant context from a mock vector store, and returns a response generated by an LLM integration.
SQL for Data Extraction
Strong SQL skills are required for data extraction and manipulation. Exercise: Write complex SQL queries involving joins, window functions, and aggregations to transform raw source data into a structured format suitable for training or inference in an AI model.

Your preparation checklist

Build a RAG pipeline project
Develop a sample end-to-end data pipeline using Python, LangChain, and a vector database. Demonstrate your ability to ingest data, perform EDA with pandas, and integrate an LLM (e.g., OpenAI or LLaMA) to answer queries based on the processed dataset.
Prepare a technical walkthrough of data automation
Create a portfolio entry or presentation detailing a reusable Python module you developed for data ingestion or ETL. Highlight how you ensured data quality, observability, and performance optimization within the pipeline.
Review AI orchestration frameworks
Prepare to discuss your practical experience or theoretical understanding of LangChain, AutoGen, or CrewAI. Be ready to explain how you would use these tools to build agentic workflows or manage complex AI interactions.
Formulate questions on data observability
Prepare specific questions regarding Virtusa's current data-to-AI stack, such as: 'What tools are currently used for production monitoring and data observability in your AI pipelines?' or 'How does the team handle data drift and quality assurance for Generative AI applications?'
Demonstrate SQL and data modeling proficiency
Be prepared to discuss complex SQL queries and data modeling strategies used to support AI systems. Review how you structure data for efficient retrieval in RAG systems versus traditional analytical workflows.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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