New opportunity

Lead Software Engineer

Virtusa · Singapore

About this role

The Lead Software Engineer role at Virtusa is a Python-focused Data Engineering position dedicated to building scalable data-intensive systems. The primary objective is to develop robust data infrastructure that enables AI and Generative AI applications. Responsibilities include building reusable Python modules for data ingestion and pipeline automation, performing exploratory data analysis (EDA), and optimizing workflows for performance and reliability. You will be responsible for supporting data requirements for ML and Generative AI systems, building APIs for downstream AI applications, and ensuring data quality and observability. Key technical requirements include hands-on experience with Python data libraries such as pandas, NumPy, Matplotlib, scikit-learn, and PyTorch. You will also be involved in preparing datasets for LLM-based systems and integrating AI models into pipelines, including workflows for RAG and AI agents using frameworks like LangChain, AutoGen, and CrewAI. Experience working with model providers such as OpenAI, Anthropic, LLaMA, and Mistral is required. Strong SQL skills and an understanding of data modeling are essential. Preferred qualifications include experience with big data or distributed processing systems, vector databases, embedding-based retrieval, cloud platforms (AWS, Azure, GCP), and production monitoring 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 Infrastructure for AI
The primary objective is to build robust, reusable Python modules for data ingestion and pipeline automation. You must demonstrate the ability to design systems that handle large datasets efficiently while ensuring high performance, reliability, and observability for downstream AI and Generative AI applications.
Generative AI Pipeline Integration
You are required to prepare datasets for LLM-based systems and integrate AI models into production pipelines. This includes implementing RAG workflows and AI agent frameworks like LangChain, AutoGen, and CrewAI, while maintaining compatibility with various model providers such as OpenAI, Anthropic, LLaMA, and Mistral.
Data Quality and API Development
Beyond pipeline construction, you must ensure data quality and consistency across all stages. You will be responsible for building APIs that serve data to downstream AI applications, requiring strong SQL skills and a deep understanding of data modeling and analytics workflows.

Likely challenges to think through

Balancing Performance with AI Orchestration
Integrating complex AI orchestration frameworks like LangChain or AutoGen into standard data pipelines can introduce latency and reliability issues. Prepare by discussing how you optimize Python-based workflows for performance while managing the overhead of external model API calls and agentic logic.
Data Observability in Non-Deterministic Systems
Ensuring data quality for LLM-based systems is difficult because model outputs are non-deterministic. You should be prepared to discuss strategies for monitoring data drift, validating embedding-based retrieval, and maintaining observability in pipelines that feed into vector databases and generative models.

Practice your answers

Technical practice

Optimizing Python Data Pipelines

What to coverDiscuss strategies for optimizing data ingestion and processing workflows using pandas and NumPy. Focus on memory management, vectorization techniques, and handling large-scale datasets that exceed local memory capacity.

RAG System Architecture

What to coverExplain the components required to build a Retrieval-Augmented Generation (RAG) system. Address how you would integrate vector databases, embedding models, and orchestration frameworks like LangChain to ensure accurate and scalable retrieval.

Data Quality and Observability

What to coverDescribe how you implement observability in production data pipelines. Discuss tools or methodologies for monitoring data drift, pipeline latency, and ensuring data consistency before it reaches downstream AI models.

AI Agent Orchestration

What to coverCompare the use cases for frameworks like AutoGen and CrewAI in building agentic workflows. Explain how you would manage state, tool usage, and communication between multiple AI agents in a production environment.

SQL and Data Modeling for AI

What to coverHow do you approach data modeling to support both traditional analytics and modern AI/ML requirements? Discuss the trade-offs between relational schemas and the structures needed for embedding-based retrieval.

Integrating LLM Providers

What to coverWhen building pipelines that interact with multiple model providers like OpenAI, Anthropic, or LLaMA, how do you handle abstraction, error handling, and rate limiting? Focus on building reusable modules for model interaction.

Deep Learning with PyTorch

What to coverDescribe your experience using PyTorch for model experimentation. Discuss how you prepare and structure datasets for training or fine-tuning, and how you integrate these models into existing data pipelines.

Distributed Processing Systems

What to coverIf a dataset is too large for a single node, what distributed processing frameworks or cloud-native approaches would you recommend? Discuss the challenges of maintaining data integrity across distributed nodes.

Behavioural practice

Handling Technical Ambiguity

What to coverDescribe a situation where you had to build a data infrastructure for an AI use case with evolving requirements. How did you ensure the system remained flexible and scalable?

Cross-Functional Collaboration

What to coverProvide an example of how you have collaborated with data scientists or AI researchers to translate their model requirements into robust, production-ready data pipelines.

Prioritizing Technical Debt

What to coverTell me about a time you had to balance the need for rapid deployment of an AI feature with the long-term need for code reusability and system reliability. How did you decide what to prioritize?

Managing Production Incidents

What to coverDescribe a time when a critical data pipeline failed in production. What was your process for identifying the root cause, communicating with stakeholders, and implementing a permanent fix?

Sharpen your knowledge

Topics to prepare

Retrieval-Augmented Generation (RAG) Architectures
Review the conceptual flow of RAG systems, specifically how data ingestion, embedding generation, and vector retrieval integrate with LLMs. This is critical for the role's focus on enabling Generative AI applications and building data-to-AI pipelines.
Data Observability and Pipeline Reliability
Study strategies for ensuring data quality, consistency, and observability in automated pipelines. The role requires maintaining robust infrastructure, making it essential to understand how to monitor data health and performance in production environments.
AI Agentic Workflows
Explore the conceptual differences between standard LLM chains and agentic workflows using frameworks like AutoGen or CrewAI. Understanding how agents interact with tools and data is a core requirement for the advertised Generative AI enablement tasks.

Skills to revise

Python Data Engineering with Pandas and PyTorch
Practice building modular Python scripts that perform ETL tasks, specifically using Pandas for data manipulation and PyTorch for preparing datasets for AI models. This directly addresses the mandatory hands-on requirement for data-intensive system development.
SQL for Complex Data Modeling
Execute advanced SQL queries involving joins, window functions, and data aggregation to support analytics workflows. Strong SQL skills are explicitly required for data extraction and manipulation in this role.
Orchestration with LangChain
Develop a prototype pipeline using LangChain to integrate a model provider (e.g., OpenAI or LLaMA) with a data source. This exercise demonstrates the ability to build the downstream AI services requested in the job description.

Your preparation checklist

Demonstrate Python Data Engineering Proficiency
Prepare a code portfolio or walkthrough showcasing reusable Python modules for ETL pipelines. Focus on demonstrating efficient use of pandas and NumPy for large dataset manipulation, and include examples of how you have optimized data ingestion workflows for performance.
Build a RAG or AI Agent Prototype
Develop a small-scale project using LangChain or AutoGen that integrates with a model provider like OpenAI or LLaMA. Document your process for structuring data for LLM consumption and explain how you handled embedding-based retrieval or vector database integration.
Review SQL and Data Modeling Skills
Be prepared to discuss complex SQL queries and data modeling strategies for AI-ready infrastructure. Review your experience with schema design for both relational and NoSQL databases to ensure you can support downstream AI application requirements.
Prepare for AI Pipeline Integration Discussions
Reflect on your experience integrating PyTorch models or scikit-learn workflows into production pipelines. Be ready to explain how you ensure data quality, observability, and monitoring in systems that feed data into Generative AI applications.
Formulate Technical Questions for the Interviewers
Prepare questions regarding the specific cloud environment (AWS, Azure, or GCP) used at Virtusa, the current scale of their data-intensive systems, and the team's approach to managing data observability for their AI and LLM-based production 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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