Added Resume Analyzer and Sample version
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"import requests\n",
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"from dotenv import load_dotenv\n",
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"from bs4 import BeautifulSoup\n",
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"from IPython.display import Markdown, display\n",
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"from openai import OpenAI\n",
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"\n",
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"# If you get an error running this cell, then please head over to the troubleshooting notebook!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7b87cadb-d513-4303-baee-a37b6f938e4d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load environment variables in a file called .env\n",
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"\n",
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"load_dotenv(override=True)\n",
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"api_key = os.getenv('OPENAI_API_KEY')\n",
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"\n",
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"# Check the key\n",
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"\n",
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"if not api_key:\n",
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" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
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"elif not api_key.startswith(\"sk-proj-\"):\n",
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" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
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"elif api_key.strip() != api_key:\n",
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" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
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"else:\n",
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" print(\"API key found and looks good so far!\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "019974d9-f3ad-4a8a-b5f9-0a3719aea2d3",
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"metadata": {},
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"outputs": [],
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"source": [
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"openai = OpenAI()\n",
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"\n",
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"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n",
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"# If it STILL doesn't work (horrors!) then please see the Troubleshooting notebook in this folder for full instructions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "abdb8417-c5dc-44bc-9bee-2e059d162699",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n",
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"\n",
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"system_prompt = \"You are a high profile professional resume analyst and assist users with highlighting gaps in a very formed resume and provide direction to make the resume eye catching to the recruiters \\\n",
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"and employers.\"\n",
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"\n",
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"user_prompt = \"\"\"Analyze the resume details to do the following: \\\n",
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"1. Assess the resume to highlight areas of improvement. \\ \n",
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"2. Create a well formed resume.\n",
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"\n",
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"Name: Sam Burns\n",
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"\n",
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"PROFESSIONAL SUMMARY\n",
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"Experienced Data and AI Architect with over 10 years of expertise designing scalable data platforms, integrating cloud-native solutions, and deploying AI/ML systems across enterprise environments. Proven track record of aligning data architecture with business strategy, leading cross-functional teams, and delivering high-impact AI-driven insights.\n",
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"\n",
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"CORE SKILLS\n",
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"\n",
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"Data Architecture: Lakehouse, Data Mesh, Delta Lake, Data Vault\n",
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"\n",
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"Cloud Platforms: Azure (Data Factory, Synapse, ML Studio), AWS (S3, Glue, SageMaker), Databricks\n",
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"\n",
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"Big Data & Streaming: Spark, Kafka, Hive, Hadoop\n",
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"\n",
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"ML/AI Tooling: MLflow, TensorFlow, Scikit-learn, Hugging Face Transformers\n",
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"\n",
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"Programming: Python, SQL, PySpark, Scala, Terraform\n",
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"\n",
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"DevOps: CI/CD (GitHub Actions, Azure DevOps), Docker, Kubernetes\n",
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"\n",
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"Governance: Data Lineage, Cataloging, RBAC, GDPR, Responsible AI\n",
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"\n",
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"PROFESSIONAL EXPERIENCE\n",
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"\n",
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"Senior Data & AI Architect\n",
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"ABC Tech Solutions — New York, NY\n",
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"Jan 2021 – Present\n",
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"\n",
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"Designed and implemented a company-wide lakehouse architecture on Databricks, integrating AWS S3, Redshift, and real-time ingestion from Kafka.\n",
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"\n",
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"Led architecture for a predictive maintenance platform using sensor data (IoT), Spark streaming, and MLflow-managed experiments.\n",
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"\n",
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"Developed enterprise ML governance framework ensuring reproducibility, fairness, and compliance with GDPR.\n",
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"\n",
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"Mentored 6 data engineers and ML engineers; led architectural reviews and technical roadmap planning.\n",
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"\n",
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"Data Architect / AI Specialist\n",
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"Global Insights Inc. — Boston, MA\n",
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"Jun 2017 – Dec 2020\n",
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"\n",
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"Modernized legacy data warehouse to Azure Synapse-based analytics platform, reducing ETL latency by 40%.\n",
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"\n",
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"Built MLOps pipelines for customer churn prediction models using Azure ML and ADF.\n",
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"\n",
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"Collaborated with business units to define semantic layers for self-service analytics in Power BI.\n",
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"\n",
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"Data Engineer\n",
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"NextGen Analytics — Remote\n",
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"Jul 2013 – May 2017\n",
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"\n",
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"Developed ETL pipelines in PySpark to transform raw web traffic into structured analytics dashboards.\n",
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"\n",
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"Integrated NLP models into customer support workflows using spaCy and early versions of Hugging Face.\n",
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"\n",
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"Contributed to open-source tools for Jupyter-based analytics and data catalog integration.\n",
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"\n",
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"EDUCATION\n",
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"M.S. in Computer Science – Carnegie Mellon University\n",
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"B.S. in Information Systems – Rutgers University\n",
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"\n",
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"CERTIFICATIONS\n",
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"\n",
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"Databricks Certified Data Engineer Professional\n",
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"\n",
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"Azure Solutions Architect Expert\n",
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"\n",
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"AWS Certified Machine Learning – Specialty\n",
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"\n",
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"PROJECTS & CONTRIBUTIONS\n",
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"\n",
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"llm_engineering (GitHub): Developed and maintained hands-on LLM course materials and community contributions framework.\n",
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"\n",
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"Real-time AI PoC: Designed Kafka-Spark pipeline with Azure OpenAI Service for anomaly detection on IoT streams.\n",
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"\n",
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"Contributor to Hugging Face Transformers – integration examples for inference pipelines\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4e6a8730-c3ad-4243-a045-0acba2b5ebcf",
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"metadata": {},
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"outputs": [],
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"source": [
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"messages = [\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "21ed95c5-7001-47de-a36d-1d6673b403ce",
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"metadata": {},
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"outputs": [],
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"source": [
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"# To give you a preview -- calling OpenAI with system and user messages:\n",
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"\n",
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"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3d926d59-450e-4609-92ba-2d6f244f1342",
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"metadata": {},
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"outputs": [],
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"source": [
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"# A function to display this nicely in the Jupyter output, using markdown\n",
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"\n",
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"display(Markdown(response.choices[0].message.content))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eeab24dc-5f90-4570-b542-b0585aca3eb6",
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"metadata": {},
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"source": [
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"# Sharing your code\n",
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"\n",
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"I'd love it if you share your code afterwards so I can share it with others! You'll notice that some students have already made changes (including a Selenium implementation) which you will find in the community-contributions folder. If you'd like add your changes to that folder, submit a Pull Request with your new versions in that folder and I'll merge your changes.\n",
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"\n",
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"If you're not an expert with git (and I am not!) then GPT has given some nice instructions on how to submit a Pull Request. It's a bit of an involved process, but once you've done it once it's pretty clear. As a pro-tip: it's best if you clear the outputs of your Jupyter notebooks (Edit >> Clean outputs of all cells, and then Save) for clean notebooks.\n",
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"\n",
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"Here are good instructions courtesy of an AI friend: \n",
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"https://chatgpt.com/share/677a9cb5-c64c-8012-99e0-e06e88afd293"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f4484fcf-8b39-4c3f-9674-37970ed71988",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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