Advanced Prompt Design Engineer

2 weeks ago

alwar, India beBeeArtificial Full time

AI Engineer Internship OpportunityThis is an exciting opportunity to work as an AI Engineer Intern. You will be part of a team that pushes the boundaries of what LLMs can do by designing, testing, and optimizing prompts, building multi-step prompt pipelines, writing scaffolding code around LLM calls, benchmarking outputs, and integrating AI features into real-world products using Python and NLP techniques.Key Responsibilities:Prompt Crafting: Design, edit, and refine prompts for different use cases and models.Prompt Chaining: Break down complex tasks into smaller subtasks and build effective multi-step prompt workflows (e.g., summarization → critique → rewrite).Benchmarking & Evaluation: Use Python (scripts and notebooks) to run automated performance benchmarks based on KPIs like accuracy, cost, and latency.Feature Building: Write Python scaffolding to integrate LLM calls into usable product features and pipelines.Hybrid NLP: Use traditional NLP techniques (e.g., regex, spaCy, NLTK) alongside LLMs to improve output quality, preprocessing, or efficiency.Iterative Improvement: Run A/B tests, gather output samples, and tweak prompts or logic based on failure cases and edge conditions.KPI Optimization: Ensure prompt chains and model outputs meet goals like quality, relevance, length, and compute cost.Model Awareness: Stay updated with the latest in GPT, Claude, Gemini, and open-source LLMs.Tooling & Automation: Build or use lightweight tooling for prompt testing, logging, and result comparison.Documentation: Maintain a structured prompt and workflow logbook with evaluations, learnings, and architecture.Strong Python scripting and automation experience.Experience using OpenAI or other LLM APIs.Ability to build small-scale tools or workflows that integrate and manage prompt-based logic.Understanding of prompt chaining or multi-step reasoning with LLMs.Familiarity with Jupyter/Colab for fast prototyping.Awareness of basic NLP techniques and when to combine them with LLM outputs.Comfort with debugging and improving outputs using test inputs and edge cases.Strong communication and analytical skills.Familiarity with basic evaluation techniques (BLEU, ROUGE, token count, etc.).Duration: 6 monthsIdeal Candidates:Final-year students who have semester break for internship or recent grads ambitious about AI/LLMs and looking to develop real-world AI engineering, prompt design, and hybrid NLP-LLM skills.

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