AI Engineer Intern

4 days ago

Rajkot, India Alchemic (previously Echo) Full time

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.30,000/month Duration: 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.

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