New opportunity

AI/ML Data Scientist

LiveMindz · Chennai, India

About this role

The AI/ML Data Scientist will be responsible for developing, deploying, and optimizing machine learning solutions while collaborating with business and technical stakeholders. The role involves translating business problems into data science solutions, performing data preprocessing, feature engineering, and exploratory data analysis. Responsibilities include developing and optimizing models for classification, regression, clustering, and forecasting, as well as working on Generative AI, LLM, RAG, and NLP-based solutions. The candidate will build Python-based data science solutions, create reusable ML pipelines, deploy models into production, and monitor their performance. Key requirements include 5-8 years of experience, strong expertise in Python, statistics, and machine learning, and the ability to communicate technical findings to stakeholders. Preferred qualifications include experience with deep learning frameworks, MLOps tools, and cloud-based AI services.

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

Skills for this role

PythonMachine LearningPandasNumPyScikit-learnSQLStatisticsTensorFlowPyTorchNLPComputer VisionGenerative AILLMsRAGVector databasesPrompt EngineeringMLflowKubeflowDockerKubernetesBigQueryDatabricksSparkCloud AI/ML services

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

End-to-End Model Lifecycle Management
The role requires not just model development but the creation of reusable ML pipelines and deployment into production. You must demonstrate proficiency in MLOps practices, including model monitoring, tuning, and validation to ensure sustained performance after deployment.
Translating Business Requirements into Technical Solutions
A core expectation is the ability to bridge the gap between business problems and data science outcomes. You must be prepared to gather requirements from non-technical stakeholders and articulate complex model findings in a way that informs business decision-making.
Advanced Generative AI and NLP Integration
Beyond traditional machine learning, the role emphasizes modern AI capabilities including LLMs, RAG, and vector databases. You should be ready to apply these technologies to solve specific business use cases rather than just building models in isolation.

Likely challenges to think through

Balancing Traditional ML with Rapidly Evolving GenAI
The role demands expertise in both foundational statistical modeling and cutting-edge GenAI/LLM frameworks. The challenge lies in selecting the right tool for the problem—knowing when a traditional regression model is more appropriate than a complex LLM-based solution.
Stakeholder Communication in a Hybrid Environment
Collaborating with diverse teams including Data Engineers, Product Managers, and business stakeholders in a hybrid work setting requires high-level communication skills. You must be prepared to manage expectations regarding model limitations and project timelines effectively.

Practice your answers

Technical practice

Designing RAG Pipelines

What to coverExplain the architecture of a Retrieval-Augmented Generation (RAG) system. Discuss the trade-offs between different vector database indexing strategies and how you handle context window limitations when retrieving information for LLMs.

ML Pipeline Reproducibility

What to coverDescribe your approach to building reusable ML pipelines. Focus on how you ensure version control for both code and data, and how tools like MLflow or Kubeflow facilitate tracking experiments and model lineage.

Model Deployment and Monitoring

What to coverWhat is your strategy for deploying models into production environments using Docker and Kubernetes? Detail how you monitor for data drift and performance degradation after deployment.

Feature Engineering for Large Datasets

What to coverWhen working with large-scale data in Spark or Databricks, how do you approach feature engineering? Discuss techniques for handling high-cardinality features and ensuring feature consistency between training and inference.

Statistical Validation of Models

What to coverBeyond standard accuracy metrics, how do you statistically validate a model's performance for classification or regression tasks? Explain how you determine if a model is ready for production based on statistical significance.

Optimization of Deep Learning Models

What to coverDiscuss techniques for optimizing models built in TensorFlow or PyTorch. Address strategies for reducing inference latency and memory footprint, such as quantization or pruning.

Prompt Engineering Best Practices

What to coverHow do you systematically evaluate and improve prompts for LLM-based solutions? Suggestion: Focus on iterative testing, few-shot prompting, and chain-of-thought techniques to improve output reliability.

Handling Imbalanced Data

What to coverDescribe your methodology for addressing class imbalance in classification problems. Compare techniques like resampling, synthetic data generation, or adjusting loss functions in your model training.

Behavioural practice

Translating Business Needs to Technical Solutions

What to coverDescribe a time you had to translate a vague business requirement into a concrete machine learning project. Explain how you defined success metrics and aligned them with stakeholder expectations.

Communicating Technical Findings

What to coverGive an example of how you explained a complex model outcome or technical limitation to a non-technical stakeholder. Focus on how you adapted your communication style to ensure they understood the business impact.

Cross-functional Collaboration

What to coverDescribe a situation where you had to work closely with Data Engineers or Software Engineers to deploy a model. How did you resolve technical disagreements or workflow bottlenecks?

Managing Model Failure in Production

What to coverTell me about a time a model performed poorly after deployment. How did you identify the root cause, communicate the issue to stakeholders, and implement a fix?

Sharpen your knowledge

Topics to prepare

Generative AI and RAG Architecture
Review the conceptual framework of Retrieval-Augmented Generation (RAG). Exercise: Design a high-level architecture diagram for a document-based Q&A system, identifying where vector databases and LLMs interact. This is directly relevant to the role's requirement for building GenAI and RAG-based solutions.
MLOps Lifecycle and Production Deployment
Study the end-to-end ML lifecycle, focusing on model monitoring, versioning, and deployment strategies. Exercise: Outline a strategy for tracking model performance drift in production and the steps for automated retraining. This addresses the requirement to deploy models and monitor their performance.
Statistical Modeling and Business Translation
Focus on mapping business KPIs to statistical metrics. Exercise: Take a hypothetical business problem, such as customer churn, and define the target variable, appropriate evaluation metrics (e.g., precision vs. recall), and the statistical validation process. This is essential for the role's focus on translating business problems into technical solutions.

Skills to revise

Python-based ML Pipeline Development
Practice building modular, reusable data pipelines using Scikit-learn and Pandas. Exercise: Create a script that automates data preprocessing, feature engineering, and model training for a classification task. This aligns with the requirement to build reusable ML pipelines.
Vector Database Integration
Gain hands-on experience with vector search concepts. Exercise: Implement a simple prototype using a vector database to store and retrieve embeddings for a text dataset. This is a practical skill requested for the RAG-based solutions mentioned in the job description.
Model Performance Optimization
Practice hyperparameter tuning and model validation techniques. Exercise: Use cross-validation and grid search to optimize a regression or classification model, documenting the impact on performance metrics. This directly supports the responsibility of optimizing machine learning models.

Your preparation checklist

Prepare a portfolio of production-grade ML pipelines
Select two past projects where you moved a model from development to production. Prepare to discuss your use of MLOps tools like MLflow or Kubeflow, containerization with Docker/Kubernetes, and how you monitored model performance post-deployment.
Develop a RAG and LLM case study
Create a concise walkthrough of a project involving Generative AI or RAG. Be ready to explain your choice of vector databases, prompt engineering strategies, and how you handled data preprocessing for NLP tasks.
Synthesize technical findings for non-technical stakeholders
Prepare a summary of a complex statistical or machine learning problem you solved. Practice explaining the business impact, the trade-offs made during feature engineering, and how you communicated the model's limitations to stakeholders.
Review statistical foundations and model optimization
Refresh your knowledge on classification, regression, and clustering algorithms. Be prepared to discuss how you perform model tuning, validation, and feature engineering to improve performance metrics in real-world datasets.
Formulate strategic questions for the interviewers
Prepare questions regarding the current data stack at LiveMindz, such as how they manage cloud-based AI services, the typical collaboration workflow between data scientists and data engineers, and the primary business challenges they aim to solve with GenAI.

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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