New opportunity

LLM Model Developer

Accenture · Bengaluru, India

About this role

As an LLM Model Developer, you will be responsible for fine-tuning Large Language Models with a focus on instruction fine-tuning and domain adaptation to enhance model relevance and performance. You will act as a Subject Matter Expert (SME), collaborating with cross-functional teams to drive key decisions, mentor junior staff, and implement best practices in model development. Responsibilities include analyzing model outputs, iterating on training processes, and optimizing performance for specific contexts. Requirements: Minimum 5 years of experience in Large Language Models. 15 years of full-time education. Proficiency in NLP techniques, model evaluation metrics, and performance tuning. Experience with Python and frameworks such as TensorFlow or PyTorch. Ability to handle large datasets and implement data preprocessing techniques. This role is based at the Bengaluru office.

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

Skills for this role

Large Language Models (LLMs)Natural Language Processing (NLP)Model evaluationPerformance tuningPythonTensorFlowPyTorchData preprocessing

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

Instruction Fine-Tuning and Domain Adaptation
The core technical mandate is to improve model relevance for specific contexts. You must demonstrate expertise in adapting pre-trained models to niche domains, which requires a deep understanding of how to curate high-quality datasets and adjust training objectives to align model behavior with specific user requirements.
Technical Leadership and Mentorship
As a Subject Matter Expert, you are expected to drive key decisions across multiple teams and mentor junior staff. This requires balancing hands-on model development with the ability to communicate complex technical strategies to cross-functional stakeholders and foster team growth.
Rigorous Model Evaluation and Optimization
You must implement robust evaluation metrics to analyze model outputs and iterate on training processes. This priority focuses on the ability to diagnose performance bottlenecks and apply systematic tuning techniques to ensure models meet production-grade standards.

Likely challenges to think through

Balancing Model Performance with Domain Constraints
Domain adaptation often risks catastrophic forgetting or overfitting to small, specialized datasets. Prepare by discussing your methodology for maintaining general model capabilities while achieving high performance in a specific, constrained context.
Managing Cross-Functional Decision Making
Operating as an SME across multiple teams can lead to conflicting requirements or technical debt. Prepare to demonstrate how you navigate competing priorities and justify your technical decisions to non-technical stakeholders or teams with different project goals.

Practice your answers

Technical practice

Instruction Fine-Tuning Methodologies

What to coverExplain the difference between supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) in the context of instruction tuning. Discuss how you select the appropriate dataset composition to ensure model alignment with specific task requirements.

Domain Adaptation Strategies

What to coverDescribe your approach to adapting a pre-trained LLM to a specialized domain, such as legal or medical text. Address the trade-offs between full parameter fine-tuning and parameter-efficient fine-tuning (PEFT) methods like LoRA or QLoRA.

Model Evaluation Metrics

What to coverBeyond standard perplexity, how do you evaluate the performance of an instruction-tuned model? Suggest metrics or frameworks for assessing factual accuracy, hallucination rates, and adherence to specific formatting constraints.

Data Preprocessing for Large Datasets

What to coverDetail your pipeline for cleaning and tokenizing large-scale datasets for LLM training. Explain how you handle data quality issues, such as deduplication, filtering low-quality content, and managing token length constraints.

Performance Tuning and Optimization

What to coverWhen a model fails to meet latency or throughput requirements in production, what diagnostic steps do you take? Discuss techniques like model quantization, pruning, or knowledge distillation to optimize inference performance.

Framework Proficiency: PyTorch vs. TensorFlow

What to coverCompare the advantages of using PyTorch versus TensorFlow for custom LLM training loops. Suggestion: Be prepared to discuss how you manage memory efficiency and distributed training across multiple GPUs in your preferred framework.

Handling Catastrophic Forgetting

What to coverHow do you mitigate catastrophic forgetting when fine-tuning a model on a new domain? Discuss techniques such as replay buffers, elastic weight consolidation, or mixing original training data with new domain-specific data.

Behavioural practice

Mentoring and Technical Leadership

What to coverDescribe a time when you had to mentor a junior team member who was struggling with a complex model development task. Explain the steps you took to guide them while ensuring the project timeline remained on track.

Cross-Functional Decision Making

What to coverProvide an example of a situation where you had to influence a cross-functional team to adopt a specific technical approach for model development. How did you communicate the trade-offs to stakeholders who may not have a deep technical background?

Managing Technical Disagreements

What to coverTell me about a time you disagreed with a team member or stakeholder regarding a model architecture or training strategy. How did you resolve the conflict and what was the final outcome for the project?

Iterative Problem Solving

What to coverDescribe a project where initial model performance was unsatisfactory. Walk me through your iterative process for analyzing the outputs, identifying the root cause, and implementing changes that led to improved performance.

Sharpen your knowledge

Topics to prepare

Instruction Fine-Tuning and Domain Adaptation
Focus on the conceptual shift from pre-training to fine-tuning. Exercise: Outline a strategy for adapting a base model to a specific industry domain (e.g., legal or medical) using instruction-tuning datasets. This is directly relevant to the role's core responsibility of enhancing model relevance in specific contexts.
Model Evaluation Metrics
Understand how to quantify model performance beyond simple accuracy. Exercise: Compare and contrast metrics like ROUGE, BLEU, and perplexity, and explain when to use human-in-the-loop evaluation for LLMs. This is essential for the requirement to analyze model outputs and iterate on training processes.

Skills to revise

Data Preprocessing for LLMs
Practice cleaning and tokenizing large-scale datasets. Exercise: Write a Python script using a library like Hugging Face Datasets to filter, deduplicate, and format a raw text corpus for instruction fine-tuning. This addresses the explicit requirement to handle large datasets.
Performance Tuning with PyTorch or TensorFlow
Gain hands-on experience with memory-efficient training techniques. Exercise: Implement a training loop using LoRA (Low-Rank Adaptation) or QLoRA to fine-tune a small open-source model on a GPU. This is highly relevant to the role's focus on optimizing performance and implementing best practices.

Your preparation checklist

Prepare a technical case study on domain adaptation
Develop a structured walkthrough of a past project where you performed instruction fine-tuning or domain adaptation on an LLM. Focus on your methodology for data preprocessing, the specific evaluation metrics used to measure performance gains, and how you addressed model relevance in a specific context.
Demonstrate proficiency in Python and deep learning frameworks
Create a portfolio or code repository showcasing your ability to implement training pipelines using PyTorch or TensorFlow. Include examples of handling large datasets and custom data preprocessing scripts that demonstrate your technical depth in preparing data for LLM training.
Formulate examples of SME leadership and mentorship
Prepare specific anecdotes that highlight your experience as a Subject Matter Expert. Focus on instances where you mentored junior staff, drove technical decision-making across cross-functional teams, and implemented best practices for model development within a larger organization.
Develop targeted questions for the interviewers
Prepare insightful questions regarding the specific domains Accenture is targeting for model adaptation, the current infrastructure used for model evaluation, and how the team balances rapid iteration cycles with maintaining high model performance standards.

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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