The Lead Architect will be responsible for designing and developing automation frameworks for shift-left and no-code/less-code environments. This role requires hands-on technical leadership in automation architecture using Java, Selenium, and Python, alongside a strong focus on Generative AI implementation. Responsibilities: Design and implement automation frameworks using design patterns such as Factory, Abstract Factory, Command, Builder, and Singleton. Lead automation architecture design and implementation while ensuring hands-on involvement in coding and scripting. Integrate automation solutions into CI/CD pipelines and collaborate with cross-functional teams. Provide technical mentorship to junior engineers, conduct code reviews, and ensure adherence to best practices. Interface with business and engineering stakeholders to translate requirements into robust implementation architectures. Qualifications: Experience in designing and implementing generative models and architectures, including RAG and fine-tuning. Proficiency in Python and ML frameworks like TensorFlow or PyTorch. Understanding of generative models including GANs, VAEs, and Transformer models such as GPT and BERT. Ability to work with large datasets, including data cleaning, augmentation, and analysis. A degree in Computer Science, Machine Learning, AI, or a related field is required. Preferred: Experience with AWS/Azure and their Generative AI capabilities, as well as containerization technologies like Docker and Kubernetes.
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A focused preparation guide, built around this job’s responsibilities and requirements.
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01
Understand the role
What matters most
Automation Framework Architecture
The role demands designing scalable, maintainable automation frameworks for shift-left and no-code/less-code environments. You must demonstrate proficiency in applying structural design patterns like Factory, Abstract Factory, Command, Builder, and Singleton to ensure the architecture remains robust as requirements evolve.
Generative AI Integration
A core priority is implementing generative models, specifically RAG and fine-tuning, within automation workflows. You must be prepared to bridge the gap between traditional automation tools like Selenium and advanced AI models using Python and frameworks like TensorFlow or PyTorch.
Technical Leadership and Mentorship
As a Lead Architect, you are expected to provide hands-on coding involvement while simultaneously mentoring junior engineers. This requires balancing individual technical contributions with the responsibility of conducting code reviews and enforcing engineering best practices across the team.
CI/CD and Infrastructure Alignment
You must ensure that automation solutions are seamlessly integrated into CI/CD pipelines. This requires a strong understanding of containerization technologies like Docker and Kubernetes, as well as cloud-based AI capabilities on AWS or Azure to support enterprise-level deployment.
Likely challenges to think through
Bridging Traditional Automation with Generative AI
Integrating non-deterministic Generative AI models into deterministic automation frameworks (like Selenium) presents a significant architectural challenge. Prepare by articulating how you handle the variability of AI outputs while maintaining the stability and reliability required for automated testing pipelines.
Stakeholder Requirement Translation
Translating high-level business requirements into technical implementation architectures is complex. You may face difficulty aligning non-technical stakeholders with the constraints of AI model performance and data quality; prepare by practicing how you communicate technical trade-offs in RAG or fine-tuning projects.
Managing Large-Scale Data Pipelines
The role requires expertise in data cleaning, augmentation, and analysis for AI models. The challenge lies in managing the data lifecycle efficiently within an automation context; be ready to discuss your approach to data governance and preprocessing at scale.
03
Practice your answers
Technical practice
Automation Framework Design Patterns
What to coverExplain how you would apply Factory, Abstract Factory, and Singleton patterns to build a scalable automation framework. Discuss the trade-offs of using these patterns in a no-code or less-code environment versus traditional scripting.
Implementing RAG Architectures
What to coverDescribe the process of designing a Retrieval-Augmented Generation (RAG) pipeline. Focus on how you handle data ingestion, vector database selection, and the integration of LLMs to ensure accurate, context-aware responses.
CI/CD Integration for Automation
What to coverDetail your approach to integrating Selenium and Python-based automation suites into a CI/CD pipeline. Address how you manage environment parity, test execution speed, and reporting within a shift-left testing strategy.
Generative AI Model Selection
What to coverCompare the use cases for Transformer models like GPT and BERT versus GANs or VAEs in an enterprise automation context. Explain how you determine which model architecture is appropriate for a specific business requirement.
Fine-Tuning and Data Preparation
What to coverDiscuss the challenges of data cleaning and augmentation when preparing datasets for fine-tuning generative models. Explain how you ensure data quality and mitigate bias during the training process.
Containerization in AI Workflows
What to coverExplain how you utilize Docker and Kubernetes to deploy and scale machine learning models or automation frameworks. Discuss the benefits of containerization for maintaining consistent environments across development and production.
ML Framework Proficiency
What to coverCompare TensorFlow and PyTorch regarding their suitability for building and deploying generative models. Suggestion: Prepare to discuss specific projects where you chose one framework over the other based on performance or ecosystem support.
Shift-Left Automation Strategy
What to coverDefine what shift-left means in the context of your automation architecture. Explain how you involve developers early in the testing lifecycle and the technical tools you use to facilitate this transition.
Behavioural practice
Technical Mentorship and Code Quality
What to coverDescribe a situation where you had to mentor junior engineers while conducting code reviews. Focus on how you balanced enforcing strict coding standards with fostering a collaborative and learning-oriented environment.
Translating Business Requirements
What to coverProvide an example of a time you translated complex business requirements into a concrete technical architecture. Explain how you managed stakeholder expectations while ensuring the final solution was robust and scalable.
Handling Technical Disagreements
What to coverShare an experience where you disagreed with a cross-functional team member regarding an architectural decision. Detail how you communicated your perspective, evaluated the trade-offs, and reached a consensus.
Adapting to Emerging Technologies
What to coverDescribe a time you had to quickly learn and implement a new technology, such as a specific Generative AI capability. Explain your process for staying updated with industry trends and applying that knowledge to a project.
02
Sharpen your knowledge
Topics to prepare
Automation Framework Design Patterns
Review the implementation of Factory, Abstract Factory, Command, Builder, and Singleton patterns specifically for test automation. Exercise: Draft a class diagram for a modular automation framework that uses the Factory pattern to instantiate different browser drivers and the Command pattern to encapsulate test actions, ensuring scalability for no-code/less-code environments.
Generative AI Architectures
Deepen understanding of RAG (Retrieval-Augmented Generation) and fine-tuning workflows. Exercise: Design a high-level architecture diagram showing how a RAG pipeline integrates with a CI/CD process to validate automation script generation, focusing on how data cleaning and augmentation impact model performance.
Transformer Models and Generative AI Fundamentals
Study the mechanics of Transformer models (GPT, BERT) and generative techniques like GANs and VAEs. Exercise: Prepare a technical summary explaining the trade-offs between using pre-trained Transformer models versus fine-tuning for specific automation tasks, such as generating test cases from business requirements.
Skills to revise
Python-based ML Framework Implementation
Demonstrate proficiency in TensorFlow or PyTorch. Exercise: Build a small script that performs data cleaning and preprocessing on a sample dataset, then use a pre-trained model to perform a basic inference task relevant to automation, such as classifying test failure logs.
CI/CD Pipeline Integration
Master the integration of automation frameworks into CI/CD pipelines. Exercise: Create a configuration file (e.g., Jenkinsfile or GitHub Actions YAML) that triggers a Selenium-based test suite, incorporates containerized execution via Docker, and reports results back to a centralized dashboard.
Technical Leadership and Mentorship
Prepare for the leadership aspect of the role. Exercise: Develop a checklist for conducting effective code reviews that specifically targets the maintainability of automation scripts and adherence to the design patterns mentioned in the job description.
04
Your preparation checklist
Architect a No-Code Automation Framework
Prepare a technical walkthrough of a scalable automation framework you have designed. Explicitly demonstrate how you applied Factory, Abstract Factory, Command, Builder, or Singleton patterns to support shift-left or no-code environments using Java or Python.
Develop a Generative AI Implementation Case Study
Prepare a presentation on a project involving RAG or model fine-tuning. Detail your experience with Transformer models like GPT or BERT, and explain how you handled data cleaning and augmentation for large datasets using TensorFlow or PyTorch.
Map CI/CD and Cloud Integration Strategy
Draft a high-level architecture diagram showing the integration of automation scripts into a CI/CD pipeline. Include how you would leverage AWS or Azure cloud services and containerization tools like Docker and Kubernetes to deploy these solutions.
Prepare Technical Leadership Examples
Document specific instances where you mentored junior engineers or conducted code reviews. Be ready to discuss how you enforce coding standards and best practices within a team to ensure long-term maintainability of automation code.
Formulate Strategic Questions for Stakeholders
Prepare questions regarding the current maturity of Birlasoft's automation frameworks, the specific business problems they aim to solve with Generative AI, and how the architecture team balances immediate project delivery with long-term technical debt management.
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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