AI Engineering Intern

Role: AI Engineer Intern (Full-time Internship, Remote)

This is a remote full-time paid internship for an AI Engineer. You will help us push 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.

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.

Salary: Rs.15,000/month

Duration: 3–6 months

Must-Have Qualifications

• 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.).

Nice-to-Have

• LangChain or similar framework experience.
• Experience working with vector DBs, retrieval-augmented generation (RAG), or memory components.
• Knowledge of managing context windows, formatting outputs, or chaining across models.
• Open-source contributions, AI blog posts, or published prompt collections.

Ideal For

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. You’ll work closely with founders to build robust, high-performing AI workflows and features for production-grade products. PPO offer post successful internship.