Accenture is seeking a Prompt Engineer for its Pune office to design, create, test and refine prompts for large language models (LLMs). The role focuses on improving model responses and developing accurate, contextually relevant AI interactions through programming, prompt-testing environments and iterative evaluation.
Responsibilities
Lead the development and optimization of prompt strategies, analyzing model responses to improve effectiveness across diverse applications.
Serve as a subject-matter expert, manage and mentor the team, and make team decisions.
Collaborate with multiple teams, contribute to key decisions, solve problems across teams and align prompt engineering work with broader project goals.
Requirements
Minimum 5 years of experience in Prompt Engineering; the posting lists an experience range of 5–10 years.
Proficiency in Prompt Engineering, a strong understanding of natural language processing concepts and techniques, and experience with AI programming languages such as Python.
Familiarity with AI model playgrounds for testing and iteration, and the ability to design prompts that balance creativity and precision.
15 years of full-time education.
This is a full-time position based at the Pune office. No preferred skills are specified.
For full details about this role, click “Apply on company site” to view the employer’s original posting.
Skills for this role
Prompt EngineeringLarge Language Models (LLMs)Natural Language Processing (NLP)PythonPrompt Testing
Your skill match
Checking your profile…
YOUR NEXT STEP
Get interview-ready for this role
A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Strategic Prompt Optimization
The role requires leading the development and refinement of prompt strategies to improve LLM outputs. You must demonstrate a systematic approach to iterative testing, using AI playgrounds to balance creativity and precision while ensuring contextually relevant results across diverse applications.
Technical Leadership and Mentorship
As a subject-matter expert with 5-10 years of experience, you are expected to mentor team members and facilitate knowledge sharing. You must be prepared to make high-level team decisions and guide the technical direction of prompt engineering initiatives.
Cross-Functional Collaboration
You will act as a bridge between multiple teams, aligning prompt engineering objectives with broader project goals. Success depends on your ability to solve complex problems across teams and contribute to key decision-making processes in a large-scale corporate environment.
Likely challenges to think through
Balancing Technical Rigor with Creative Output
A core challenge is designing prompts that are both precise enough for technical accuracy and creative enough for diverse user interactions. Prepare by discussing specific frameworks or methodologies you use to evaluate and quantify the quality of LLM responses during the iterative testing process.
Navigating Ambiguity in Large-Scale Projects
Working across multiple teams often involves conflicting project requirements. You should prepare to discuss how you prioritize prompt engineering tasks when faced with competing stakeholder needs and how you maintain consistency in model behavior across different use cases.
03
Practice your answers
Technical practice
Prompt Engineering Techniques
What to coverExplain the difference between zero-shot, few-shot, and chain-of-thought prompting. Discuss how you select the appropriate technique based on the complexity of the task and the specific LLM being utilized.
Iterative Prompt Refinement
What to coverDescribe your process for evaluating and refining a prompt that is consistently producing inaccurate or irrelevant outputs. Focus on how you use model playgrounds to isolate variables and measure performance improvements.
Balancing Creativity and Precision
What to coverHow do you adjust prompt parameters or structure to balance the need for creative generation versus strict adherence to factual accuracy? Provide examples of how you manage temperature, top-p, or system instructions to achieve this balance.
Python for AI Development
What to coverDiscuss how you leverage Python to automate prompt testing or integrate LLMs into larger software workflows. Mention specific libraries or frameworks you use to handle API interactions or data processing for model evaluation.
NLP Fundamentals in Prompting
What to coverHow does your understanding of NLP concepts like tokenization, context windows, and attention mechanisms inform your prompt design? Explain how these technical constraints impact the effectiveness of your prompts.
Model Response Analysis
What to coverWhat metrics or qualitative criteria do you use to analyze model responses? Suggestion: Prepare to discuss how you build evaluation frameworks to ensure consistency across different test cases.
Behavioural practice
Mentoring and Team Leadership
What to coverDescribe a time when you had to mentor a team member or elevate the technical expertise of your group. Focus on how you shared knowledge regarding prompt engineering best practices to improve overall team output.
Cross-Functional Collaboration
What to coverProvide an example of a time you had to align your technical prompt engineering objectives with the goals of a non-technical stakeholder or a different project team. Explain how you navigated conflicting priorities to reach a successful outcome.
Decision Making Under Uncertainty
What to coverDescribe a situation where you had to make a key technical decision regarding prompt strategy without complete information. How did you evaluate the risks and justify your choice to the team?
Problem Solving Across Teams
What to coverTell me about a complex problem you identified that spanned multiple teams. Explain the steps you took to coordinate a solution and ensure the prompt engineering strategy remained consistent across the project.
02
Sharpen your knowledge
Topics to prepare
Natural Language Processing (NLP) Fundamentals
Review core NLP concepts such as tokenization, embeddings, attention mechanisms, and context windows. This is essential for understanding how LLMs interpret inputs and why specific prompt structures influence model behavior. Exercise: Explain how a transformer-based model processes a prompt and identify potential failure points like hallucination or context overflow.
Prompt Engineering Methodologies
Study advanced prompting techniques including Chain-of-Thought (CoT), Few-Shot prompting, and iterative refinement strategies. The role requires balancing creativity and precision. Exercise: Design a prompt template for a complex task and document how you would iteratively refine it based on specific output metrics or error analysis.
Team Leadership and Cross-Functional Collaboration
As an SME and team lead, you must demonstrate the ability to make high-level decisions and align technical prompt strategies with broader project goals. Exercise: Prepare a scenario where you had to resolve a technical disagreement within a team regarding prompt optimization or model selection.
Skills to revise
Python for AI Development
Demonstrate proficiency in using Python to automate prompt testing, interact with LLM APIs, and process model outputs. Exercise: Write a Python script that programmatically sends a series of prompts to an LLM API and logs the responses for performance evaluation.
AI Model Playground Utilization
Gain hands-on experience with model playgrounds to test, iterate, and debug prompts in real-time. Exercise: Use a model playground to compare the performance of different prompt variations on a specific task, focusing on accuracy, latency, and cost-efficiency.
Model Response Analysis
Develop a systematic approach to evaluating and interpreting LLM outputs to drive continuous improvement. Exercise: Create a rubric for evaluating model responses based on relevance, accuracy, and tone, then apply it to a set of generated outputs to identify specific areas for prompt adjustment.
04
Your preparation checklist
Prepare a portfolio of prompt engineering case studies
Document 3-5 specific examples where you designed, tested, and refined prompts for LLMs. Include the initial prompt, the identified performance issues, the iterative techniques used (e.g., few-shot, chain-of-thought), and the measurable improvement in model output accuracy or relevance.
Demonstrate Python proficiency in AI workflows
Prepare a code repository or walkthrough showing how you use Python to automate prompt testing, interact with model APIs, or perform data analysis on model responses. Focus on scripts that facilitate iterative evaluation and performance tracking.
Develop a framework for cross-team collaboration
Prepare to discuss your experience as a subject matter expert in a leadership capacity. Outline a methodology for aligning prompt engineering objectives with broader project goals and how you manage cross-functional communication to solve complex technical problems.
Formulate questions regarding team structure and model evaluation
Prepare thoughtful questions for the interviewers, such as: How does the team currently measure the success of prompt strategies? What is the typical workflow for cross-team collaboration on AI projects? How are prompt-testing environments integrated into the broader development lifecycle at Accenture?
Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
Share this opportunityKnow someone who would be a great fit?