New opportunity

AI/ML Engineer - Senior Consultant

Winfo Solutions · Hyderabad, India

About this role

The AI/ML Engineer - Senior Consultant will be responsible for building and deploying advanced AI/ML and data solutions. The role focuses on developing robust data pipelines and implementing RAG-based applications to solve complex business problems. Responsibilities: Develop and maintain ETL/ELT data pipelines using Databricks. Build and implement RAG-based AI applications. Work with LLMs, embeddings, vector databases, and prompt engineering. Prepare, clean, transform, and validate data for AI/ML applications. Develop ML/AI solutions using Python. Integrate AI models with enterprise applications and data platforms. Optimize data pipelines and AI solutions for performance and scalability. Collaborate with Data Engineering and business teams to deliver AI solutions. Requirements: 4-8 years of experience. Strong hands-on experience in Python. Strong experience with Databricks and ETL/Data Engineering. Hands-on experience with RAG architecture and LLM applications. Good knowledge of SQL and data processing. Understanding of Machine Learning and AI concepts. Preferred Qualifications: Experience with PySpark/Spark. Knowledge of Azure, AWS, or GCP. Experience with LangChain, LlamaIndex, or similar frameworks. Knowledge of MLflow and AI/ML model deployment. Experience with REST APIs 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

PythonDatabricksETLRAGSQLLLMsEmbeddingsVector DatabasesPrompt EngineeringMachine LearningPySparkSparkAzureAWSGCPLangChainLlamaIndexMLflowREST APIs

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 RAG Application Development
The role requires building functional RAG applications, which involves orchestrating the entire lifecycle from data ingestion and vectorization to prompt engineering and model integration. You must demonstrate proficiency in connecting LLMs with proprietary data via vector databases and frameworks like LangChain or LlamaIndex.
Robust Data Engineering with Databricks
A core priority is the development and maintenance of ETL/ELT pipelines within the Databricks environment. You must be prepared to handle data cleaning, transformation, and validation at scale, ensuring that the data feeding the AI models is high-quality, performant, and reliable.
Cross-Functional Collaboration and Integration
As a Senior Consultant, you are expected to bridge the gap between technical data engineering teams and business stakeholders. This involves integrating AI models into existing enterprise applications via REST APIs and ensuring that solutions are scalable and aligned with business requirements.

Likely challenges to think through

Balancing Model Performance with Data Latency
Implementing RAG architectures often introduces latency issues when querying large vector databases. Prepare by understanding how to optimize retrieval strategies and pipeline performance in Databricks to ensure the AI application remains responsive for end-users.
Managing Data Quality for LLM Context
The effectiveness of a RAG application is highly dependent on the quality of the retrieved context. You may face challenges in cleaning and structuring unstructured data for embedding. Prepare by discussing your approach to data validation and preprocessing techniques that minimize hallucinations and improve retrieval accuracy.

Practice your answers

Technical practice

Designing RAG Architectures

What to coverExplain the end-to-end flow of a RAG application, specifically how you handle document ingestion, chunking strategies, and vector database selection. Discuss how you ensure retrieval accuracy and minimize hallucinations in the generated output.

Databricks ETL Optimization

What to coverDescribe your approach to optimizing ETL pipelines within Databricks. Focus on techniques for managing large-scale data processing, such as partitioning, file format selection, and utilizing Spark/PySpark for performance improvements.

Vector Database Implementation

What to coverCompare different vector database options and explain the criteria for choosing one based on latency, scalability, and integration requirements. Address how you manage index updates and data consistency in a production environment.

Prompt Engineering Best Practices

What to coverDiscuss methodologies for iterative prompt engineering to improve LLM performance. Suggest how you evaluate prompt effectiveness and handle edge cases where the model fails to retrieve relevant context.

ML Model Deployment with MLflow

What to coverExplain how you use MLflow to manage the lifecycle of an AI model, from tracking experiments to final deployment. Focus on versioning, model registry, and ensuring reproducibility in your deployments.

Data Cleaning and Validation for AI

What to coverDetail your process for preparing raw data for AI/ML applications. Emphasize how you validate data quality and handle missing or noisy data to ensure high-quality embeddings and model performance.

Integrating AI Models with REST APIs

What to coverDescribe how you wrap AI models in RESTful APIs for enterprise application consumption. Discuss security, authentication, and rate-limiting strategies for production-grade AI services.

Scaling AI Solutions in Cloud Environments

What to coverExplain the trade-offs between different cloud-based AI services (Azure, AWS, or GCP) when deploying scalable ML solutions. Suggest how you manage infrastructure costs while maintaining high availability.

Behavioural practice

Collaborating with Cross-functional Teams

What to coverDescribe a situation where you had to work closely with Data Engineering or business stakeholders to deliver an AI solution. Focus on how you aligned technical requirements with business goals and resolved conflicting priorities.

Managing Technical Debt in Data Pipelines

What to coverProvide an example of a time you identified a performance bottleneck in a data pipeline. Explain the actions you took to optimize the system and the resulting impact on scalability or reliability.

Handling Project Ambiguity

What to coverShare an experience where you were tasked with implementing an AI solution with unclear requirements. Detail how you gathered information, defined the scope, and communicated progress to stakeholders.

Learning and Adapting to New AI Frameworks

What to coverDescribe a time you had to quickly adopt a new framework like LangChain or LlamaIndex to solve a specific problem. Explain your learning process and how you successfully integrated the tool into your workflow.

Sharpen your knowledge

Topics to prepare

RAG Architecture Fundamentals
Review the end-to-end flow of Retrieval-Augmented Generation, specifically how document chunking, embedding generation, and vector retrieval influence LLM output quality. Exercise: Design a conceptual architecture diagram for a document-based Q&A system, identifying where bottlenecks typically occur in retrieval latency.
Data Pipeline Optimization
Focus on the principles of building scalable ETL/ELT pipelines within a Databricks environment. Exercise: Analyze a hypothetical scenario where a data pipeline is failing to meet performance SLAs and outline strategies for partitioning, caching, and cluster configuration to optimize throughput.
LLM Integration Patterns
Understand how to integrate LLMs into enterprise applications using frameworks like LangChain or LlamaIndex. Exercise: Explain the trade-offs between using a managed cloud-based AI service versus deploying a model via MLflow in terms of security, cost, and scalability.

Skills to revise

Databricks and PySpark Development
Practice writing efficient PySpark code for data transformation tasks. Exercise: Write a script to perform a complex join and aggregation on a large dataset in Databricks, ensuring the code is optimized for distributed processing.
Vector Database Management
Gain hands-on experience with vector databases to store and query embeddings. Exercise: Create a small-scale prototype that indexes a set of text documents into a vector store and performs a semantic similarity search using Python.
Prompt Engineering and Validation
Develop techniques for crafting and testing prompts to improve LLM accuracy. Exercise: Create a test suite of prompts for a specific business use case and document a methodology for validating the relevance and accuracy of the generated responses.

Your preparation checklist

Architect a RAG pipeline walkthrough
Prepare a technical case study demonstrating your experience with RAG architecture. Focus on the end-to-end flow: data ingestion, chunking strategies, embedding generation, vector database selection, and retrieval optimization using frameworks like LangChain or LlamaIndex.
Demonstrate Databricks ETL proficiency
Prepare to discuss your experience building scalable ETL/ELT pipelines within Databricks. Be ready to explain how you handle data cleaning, transformation, and validation specifically for AI/ML model consumption using PySpark.
Prepare Python and SQL code samples
Curate examples of your Python code that showcase clean, modular design for ML applications. Include SQL snippets that demonstrate complex data processing or feature engineering tasks relevant to enterprise data platforms.
Develop an ML deployment strategy
Outline your approach to model lifecycle management using tools like MLflow. Explain how you ensure scalability and performance when integrating AI models into enterprise applications via REST APIs.
Formulate technical questions for the team
Prepare questions regarding the company's current cloud infrastructure (Azure, AWS, or GCP), their specific challenges with LLM latency or accuracy, and how the team collaborates between Data Engineering and business stakeholders.

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