New opportunity

LLM Engineer - Manager

PwC Acceleration Centres · Bangalore, India

About this role

The Opportunity: Join the Acceleration Center India to shape business outcomes for a diverse global client portfolio. You will work within the Data and Analytics Engineering practice, leading the design and delivery of AI solutions that transform data into actionable insights. As a Manager, you will oversee project delivery, mentor junior staff, and bridge the gap between complex technical AI capabilities and client business needs. Responsibilities: Lead LLM engineering workstreams applying machine learning, deep learning, and NLP to solve client challenges. Manage project plans, budgets, and timelines for AI initiatives. Guide teams in training, testing, and tuning language models for tasks including summarization, classification, retrieval, and generation. Review data pipelines, model outputs, and evaluation metrics to ensure reliability and performance. Apply statistical analysis and data modeling to support predictive modeling. Coordinate development across cloud-based platforms and mentor team members on workflows and troubleshooting. Build client relationships to translate business requirements into technical AI solutions. Requirements: Bachelor's degree. Minimum of 8 years of professional experience. Proficiency in English. Preferred Qualifications: Academic background in AI, Robotics, Computer Science, Data Science, Mathematics, Statistics, or related engineering fields. Hands-on experience with LLM engineering using Python, SQL, and cloud tools. Proven ability to manage client-facing AI delivery and mentor junior team members through code reviews and technical feedback.

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

Skills for this role

Large Language ModelsMachine LearningDeep LearningNatural Language ProcessingPythonSQLData ModelingStatistical AnalysisProject ManagementCloud Analytics Platforms

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

Client-Facing Technical Translation
The role requires bridging the gap between complex AI capabilities and specific business needs. You must demonstrate the ability to articulate technical trade-offs, such as model latency versus accuracy, in a language that non-technical stakeholders can understand to drive project adoption.
End-to-End LLM Lifecycle Management
You are expected to lead the full development cycle, from initial data pipeline design and model training to testing, tuning, and production deployment. Success depends on your ability to maintain high standards for model reliability and performance metrics across diverse client use cases.
Technical Mentorship and Team Leadership
As a Manager, you must actively guide junior staff through code reviews, troubleshooting, and best practices in data science workflows. You are responsible for the technical growth of your team while simultaneously managing project budgets, timelines, and deliverables.

Likely challenges to think through

Balancing Technical Rigor with Project Constraints
Managing AI initiatives often involves navigating the tension between experimental model development and strict client timelines or budgets. Prepare by discussing your methodology for prioritizing features, managing scope creep, and ensuring that technical debt is minimized while meeting delivery deadlines.
Ensuring Model Reliability in Production
Translating LLM prototypes into robust, production-ready solutions for global clients is difficult due to issues like hallucinations, data drift, and latency. Prepare by detailing your experience with evaluation frameworks, monitoring strategies, and rigorous testing protocols to ensure consistent output quality.

Practice your answers

Technical practice

LLM Fine-tuning vs. RAG

What to coverExplain the trade-offs between fine-tuning a pre-trained language model and implementing Retrieval-Augmented Generation (RAG). Discuss when each approach is appropriate for client-facing tasks like summarization or retrieval, focusing on data freshness, hallucinations, and computational cost.

Evaluating LLM Performance

What to coverDescribe the metrics and methodologies you use to evaluate LLM outputs for classification and generation tasks. Suggestion: Prepare to discuss how you balance automated metrics like ROUGE or BLEU with human-in-the-loop evaluation and domain-specific benchmarks.

Data Pipeline Reliability

What to coverHow do you ensure data quality and pipeline reliability when preparing datasets for training or fine-tuning language models? Focus on handling noisy data, schema validation, and monitoring for data drift in production environments.

Cloud-based AI Deployment

What to coverDiscuss your experience coordinating AI development across cloud-based analytics platforms. Address considerations for scalability, cost management, and security when deploying LLM-based solutions for enterprise clients.

Addressing Model Hallucinations

What to coverWhat strategies do you employ to mitigate hallucinations in LLM applications? Discuss techniques such as prompt engineering, temperature control, and grounding models in verified external knowledge bases.

Statistical Analysis in AI

What to coverHow do you integrate statistical analysis and data modeling into the AI development lifecycle? Explain how these methods support predictive modeling and help validate the performance of your AI solutions.

SQL for AI Workflows

What to coverDescribe how you utilize SQL in the context of LLM engineering, particularly for data extraction, feature engineering, or managing vector database metadata. Suggestion: Be ready to explain how you optimize complex queries for large-scale data processing.

Model Troubleshooting

What to coverWalk through your process for troubleshooting a model that is underperforming in a production environment. Focus on how you isolate issues between data quality, model architecture, and inference parameters.

Behavioural practice

Translating Technical Complexity

What to coverDescribe a time you had to explain a complex AI technical trade-off to a non-technical client. Focus on how you translated the technical constraints into business-focused recommendations to reach an agreement.

Mentoring Junior Staff

What to coverProvide an example of how you have mentored junior team members through code reviews or technical feedback. Explain how you balanced the need for high-quality deliverables with the goal of developing their technical skills.

Managing Project Delivery

What to coverTell me about a time you managed a challenging AI project timeline or budget. Describe the situation, the actions you took to keep the project on track, and the final result for the client.

Handling Conflicting Requirements

What to coverDescribe a situation where client business needs conflicted with technical feasibility in an AI project. How did you navigate this conflict to deliver a solution that satisfied the client while maintaining technical integrity?

Sharpen your knowledge

Topics to prepare

LLM Lifecycle Management
Focus on the end-to-end process of training, testing, and tuning language models for specific tasks like summarization and retrieval. Exercise: Draft a framework for evaluating model performance metrics (e.g., precision, recall, latency) in a production environment. This is relevant to the requirement of reviewing model outputs and ensuring solution reliability.
Client-Facing Technical Leadership
Practice translating complex AI technical trade-offs into business-focused recommendations for non-technical stakeholders. Exercise: Prepare a mock presentation explaining why a specific model architecture was chosen over another to solve a business problem. This aligns with the role's need to bridge the gap between AI capabilities and client business needs.
Project Management for AI Initiatives
Review methodologies for managing timelines, budgets, and deliverables within data science workstreams. Exercise: Create a hypothetical project plan for an LLM deployment, identifying potential bottlenecks in data pipelines and resource allocation. This is essential for the Manager-level responsibility of overseeing project delivery.

Skills to revise

Python for LLM Engineering
Demonstrate proficiency in building and debugging data pipelines and model integration code. Exercise: Write a script to automate the evaluation of a model's output against a ground-truth dataset. This directly addresses the hands-on requirement for Python and model testing.
Technical Mentorship and Code Review
Develop a structured approach to providing constructive feedback on code quality and AI workflows. Exercise: Review a sample piece of code for an NLP task and document specific suggestions for performance optimization and best practices. This is a core expectation for mentoring junior staff.
SQL and Data Modeling
Ensure mastery of data manipulation and schema design to support predictive modeling. Exercise: Design a database schema to store and retrieve unstructured text data for RAG (Retrieval-Augmented Generation) applications. This is relevant to the requirement for data modeling and supporting predictive analytics.

Your preparation checklist

Prepare a project case study on LLM deployment
Develop a concise walkthrough of a past project where you led the end-to-end delivery of an LLM-based solution. Focus on how you handled data pipelines, model tuning for specific tasks like summarization or retrieval, and the metrics used to evaluate performance and reliability.
Demonstrate technical leadership and mentorship
Prepare specific examples of how you have conducted code reviews, mentored junior data scientists, and managed technical workflows. Be ready to discuss your approach to providing constructive feedback and ensuring team adherence to best practices in AI development.
Articulate business-to-technical translation
Draft a narrative explaining how you have successfully translated complex client business requirements into actionable AI technical specifications. Prepare to discuss how you manage client expectations, explain technical trade-offs, and maintain project timelines and budgets.
Formulate strategic questions for the interview
Prepare thoughtful questions regarding the team's current cloud analytics stack, the typical scale of client AI initiatives at the Acceleration Centre, and how the firm balances rapid LLM innovation with the rigorous reliability standards required for global client engagements.

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

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…