| from typing import List |
| from .schemas import JobPosting, SamplePersona |
|
|
| SAMPLE_PERSONAS: List[SamplePersona] = [ |
| SamplePersona( |
| id="persona-ai-eng", |
| name="Alex Chen", |
| title="Senior AI & Machine Learning Engineer", |
| summary="5+ years experience building LLM pipelines, PyTorch models, and high-scale FastAPI microservices.", |
| resume_text="""Alex Chen | Senior AI / ML Engineer |
| Contact: alex.chen@example.com | San Francisco, CA | github.com/alexchen | linkedin.com/in/alexchen-ai |
| |
| Summary: |
| Results-driven AI & Machine Learning Engineer with 5+ years of experience designing, training, and deploying large-scale NLP, Deep Learning, and Computer Vision architectures into production. Expert in PyTorch, Hugging Face Transformers, Sentence-BERT, LangChain, RAG pipelines, FastAPI, and Dockerized microservices. Experienced in building real-time semantic search, recommendation engines, and ML training pipelines on AWS and GCP. |
| |
| Technical Skills: |
| • Programming & Core: Python (Advanced), C++, SQL, Bash, Git, Linux |
| • Machine Learning & NLP: PyTorch, TensorFlow, Scikit-Learn, XGBoost, LightGBM, Hugging Face, Transformers, Sentence-BERT, spaCy, NLTK, OpenCV |
| • LLM & GenAI: LangChain, LlamaIndex, RAG, Vector Databases (Pinecone, ChromaDB, Weaviate), OpenAI API, Prompt Engineering |
| • Cloud & MLOps: AWS (EC2, S3, SageMaker), GCP, Docker, Kubernetes, CI/CD (GitHub Actions), MLflow, DVC, Airflow |
| • Backend & Systems: FastAPI, Flask, REST API, gRPC, PostgreSQL, Redis, Microservices, Distributed Systems |
| |
| Professional Experience: |
| Senior AI Engineer | NeuralScale AI (2022 - Present) | San Francisco, CA |
| • Designed and deployed a multi-tenant LLM RAG platform serving 2M+ monthly queries using LangChain, Pinecone, and FastAPI, cutting latency by 45%. |
| • Fine-tuned open-source Transformer models (Llama 3, Mistral) using LoRA and PyTorch, achieving 94.2% domain intent accuracy. |
| • Engineered distributed feature extraction and embedding pipelines processing 50M+ documents using Sentence-BERT, Redis, and Celery. |
| • Mentored 4 junior ML engineers and established MLOps practices with automated CI/CD and MLflow tracking. |
| |
| Machine Learning Engineer | Cortex Dynamics (2019 - 2022) | Seattle, WA |
| • Developed real-time recommendation algorithms and semantic matching pipelines using XGBoost, Scikit-Learn, and Word2Vec, increasing user engagement by 28%. |
| • Built high-throughput asynchronous REST APIs using FastAPI and Docker, containerized on AWS ECS. |
| • Integrated PostgreSQL and Redis caching layers to reduce average response time from 350ms to 48ms. |
| |
| Education: |
| • Master of Science in Computer Science (AI Track) | Stanford University |
| • Bachelor of Science in Software Engineering | University of Washington |
| """ |
| ), |
| SamplePersona( |
| id="persona-fullstack", |
| name="Sarah Jenkins", |
| title="Full-Stack Web Developer (React + Python/Node)", |
| summary="4+ years developing scalable SaaS web applications with React, Next.js, FastAPI, Node.js, and PostgreSQL.", |
| resume_text="""Sarah Jenkins | Full-Stack Software Engineer |
| Contact: sarah.jenkins@example.com | London, UK | github.com/sjenkins-dev | linkedin.com/in/sarah-jenkins-dev |
| |
| Summary: |
| Dynamic Full-Stack Software Engineer with 4+ years of hands-on experience building modern, accessible, and high-performance web applications. Proficient across the entire software development lifecycle, from designing intuitive React/Next.js frontends to architecting robust backend APIs using FastAPI, Node.js, Express, and PostgreSQL. Passionate about clean code, component-driven design, and CI/CD automation. |
| |
| Technical Skills: |
| • Frontend: JavaScript (ES6+), TypeScript, React, Next.js, Vue.js, HTML5, CSS3, Tailwind CSS, Redux, Webpack, Vite |
| • Backend: Python, FastAPI, Django, Node.js, Express, REST API, GraphQL, Microservices |
| • Databases: PostgreSQL, MySQL, MongoDB, Redis, SQLite, Prisma ORM, SQLAlchemy |
| • DevOps & Tools: Docker, Git, GitHub Actions, AWS (S3, CloudFront), Linux, Agile, Scrum, Unit Testing (Jest, Pytest) |
| |
| Professional Experience: |
| Full-Stack Engineer | FinTech Horizon (2021 - Present) | London, UK |
| • Engineered core customer dashboard in Next.js, React, and Tailwind CSS, improving core web vitals and reducing page load times by 35%. |
| • Architected asynchronous REST APIs in FastAPI with Pydantic validation and JWT authentication, supporting 100k+ daily transactions. |
| • Designed PostgreSQL schema and query optimizations with Redis caching, decreasing database query latency by 40%. |
| • Built automated CI/CD deployment workflows using GitHub Actions and Docker. |
| |
| Junior Software Developer | CloudBase Digital (2019 - 2021) | Manchester, UK |
| • Built responsive frontend components using React and styled-components for an enterprise analytics SaaS. |
| • Developed RESTful endpoints using Node.js, Express, and MongoDB. |
| • Implemented unit and integration tests using Jest and Supertest, achieving 85%+ test coverage. |
| |
| Education: |
| • B.Sc. in Computer Science | University of Manchester |
| """ |
| ), |
| SamplePersona( |
| id="persona-devops", |
| name="Marcus Vance", |
| title="Lead Cloud & DevOps Engineer", |
| summary="6+ years specializing in Kubernetes, Terraform, AWS multi-region architectures, and automated CI/CD pipelines.", |
| resume_text="""Marcus Vance | Lead DevOps & Cloud Infrastructure Engineer |
| Contact: marcus.vance@example.com | Austin, TX | github.com/marcus-vance | linkedin.com/in/marcus-vance-cloud |
| |
| Summary: |
| Accomplished Cloud & DevOps Engineer with 6+ years of expertise designing, scaling, and automating resilient multi-cloud infrastructures. Proven track record implementing Infrastructure as Code (Terraform), Kubernetes cluster orchestration, zero-downtime CI/CD pipelines, and proactive observability platforms on AWS and GCP. |
| |
| Technical Skills: |
| • Cloud Platforms: AWS (EKS, EC2, S3, RDS, IAM, VPC), GCP, Microsoft Azure |
| • Containerization & Orchestration: Kubernetes, Docker, Helm, Docker Swarm, OpenShift |
| • Infrastructure as Code (IaC): Terraform, Ansible, CloudFormation |
| • CI/CD & Automation: GitHub Actions, GitLab CI, Jenkins, ArgoCD, Bash, Python |
| • Monitoring & Observability: Prometheus, Grafana, Datadog, ELK Stack (Elasticsearch, Logstash, Kibana) |
| • Networking & Security: Linux (RHEL, Ubuntu), Nginx, Istio Service Mesh, OAuth, SSL/TLS, Vault |
| |
| Professional Experience: |
| Lead DevOps Engineer | ScaleForge Systems (2021 - Present) | Austin, TX |
| • Architected multi-region AWS EKS Kubernetes clusters handling 50M+ requests daily with 99.99% uptime. |
| • Reduced infrastructure provisioning time from 2 weeks to 20 minutes by authoring modular Terraform code. |
| • Implemented GitOps deployment workflows using ArgoCD and GitHub Actions, cutting production deployment failures by 60%. |
| • Configured Prometheus, Grafana, and Datadog alerts for real-time anomaly detection and SLO tracking. |
| |
| Cloud Operations Engineer | DataStream Enterprise (2018 - 2021) | Denver, CO |
| • Managed Docker containerization of 40+ legacy services and migrated infrastructure to AWS. |
| • Built automated CI/CD pipelines with Jenkins and GitLab CI. |
| • Enforced security compliance, automated backup routines, and IAM least-privilege policies. |
| |
| Education: |
| • B.S. in Information Technology & Network Security | University of Colorado |
| • Certified Kubernetes Administrator (CKA) | AWS Certified Solutions Architect - Professional |
| """ |
| ) |
| ] |
|
|
| SAMPLE_JOBS: List[JobPosting] = [ |
| JobPosting( |
| id="job-ai-lead", |
| title="Senior AI / ML Research Engineer", |
| company="Anthropic-Style AI Labs", |
| location="San Francisco, CA / Remote", |
| type="Remote", |
| salary_range="$180,000 - $240,000", |
| required_skills=["python", "pytorch", "transformers", "sentence-bert", "nlp", "fastapi", "docker", "aws", "rag", "scikit-learn"], |
| jd_text="""Job Title: Senior AI / ML Research Engineer |
| Location: San Francisco, CA (Remote Friendly) |
| Company: NextGen Intelligence Labs |
| Salary: $180,000 - $240,000 + Equity |
| |
| About the Role: |
| We are seeking an exceptional Senior AI/ML Engineer to lead the design and deployment of cutting-edge NLP, Transformer embeddings, and RAG architectures. You will collaborate directly with our founding research team to turn state-of-the-art AI into ultra-fast, production-grade microservices. |
| |
| Key Responsibilities: |
| • Build and fine-tune large-scale Transformer models, Sentence-BERT semantic matching pipelines, and LLM inference workflows. |
| • Design scalable, low-latency REST APIs in Python using FastAPI, Docker, and Redis caching. |
| • Build automated MLOps pipelines on AWS/GCP for continuous evaluation, benchmarking, and deployment. |
| • Optimize model inference latency and vector search across millions of embeddings. |
| |
| Mandatory Requirements: |
| • 4+ years of professional AI/ML engineering experience in Python. |
| • Strong mastery of PyTorch, Scikit-Learn, XGBoost, and Hugging Face Transformers. |
| • Production experience with Sentence-BERT, spaCy, and NLP semantic similarity. |
| • Proven track record building and deploying production APIs using FastAPI, Docker, and AWS. |
| • Solid background in vector databases (Pinecone, ChromaDB) and RAG architectures. |
| """ |
| ), |
| JobPosting( |
| id="job-fullstack-dev", |
| title="Senior Full-Stack Engineer (React + Python/FastAPI)", |
| company="VentureFlow SaaS", |
| location="London, UK / Hybrid", |
| type="Hybrid", |
| salary_range="£85,000 - £110,000", |
| required_skills=["react", "nextjs", "typescript", "python", "fastapi", "postgresql", "docker", "tailwind", "rest api"], |
| jd_text="""Job Title: Senior Full-Stack Engineer |
| Location: London, UK (Hybrid - 2 days/week in office) |
| Company: VentureFlow Technologies |
| Salary: £85,000 - £110,000 + Benefits |
| |
| About the Role: |
| VentureFlow is looking for a talented Senior Full-Stack Software Engineer to build our next-generation enterprise investment platform. You will have full ownership across the modern React/Next.js frontend and high-throughput Python/FastAPI microservices. |
| |
| Key Responsibilities: |
| • Architect clean, modern, and accessible user interfaces in React, Next.js, TypeScript, and Tailwind CSS. |
| • Develop high-performance, asynchronous RESTful APIs using Python, FastAPI, and SQLAlchemy. |
| • Design and optimize PostgreSQL database schemas, indexing, and Redis caching. |
| • Write comprehensive unit and integration tests, participating in code reviews and agile sprints. |
| |
| Requirements: |
| • 4+ years experience in Full-Stack web development. |
| • Deep proficiency with React, TypeScript, and modern CSS frameworks (Tailwind). |
| • Strong backend experience with Python (FastAPI or Django) or Node.js. |
| • Strong relational database design skills with PostgreSQL. |
| • Experience with Docker, Git, and automated CI/CD pipelines. |
| """ |
| ), |
| JobPosting( |
| id="job-devops-lead", |
| title="Lead Cloud & Kubernetes Architect", |
| company="Apex Global Cloud", |
| location="New York, NY / Remote", |
| type="Remote", |
| salary_range="$170,000 - $215,000", |
| required_skills=["kubernetes", "docker", "terraform", "aws", "ci/cd", "prometheus", "grafana", "linux", "python", "ansible"], |
| jd_text="""Job Title: Lead Cloud & Kubernetes Architect |
| Location: New York, NY (100% Remote) |
| Company: Apex Cloud Infrastructure |
| Salary: $170,000 - $215,000 + Bonus |
| |
| About the Role: |
| We are hiring a Lead DevOps / Cloud Infrastructure Architect to scale our globally distributed cloud footprint. You will lead Kubernetes orchestration, Infrastructure as Code, and automated multi-region deployments on AWS. |
| |
| Key Responsibilities: |
| • Architect, operate, and scale production Kubernetes (EKS) clusters handling high-traffic enterprise workloads. |
| • Author, modularize, and maintain infrastructure using Terraform and Ansible. |
| • Design zero-downtime CI/CD pipelines using GitHub Actions and ArgoCD. |
| • Maintain end-to-end observability using Prometheus, Grafana, and Datadog. |
| |
| Requirements: |
| • 5+ years experience in DevOps, Site Reliability, or Cloud Engineering. |
| • Expert-level knowledge of Kubernetes (EKS, GKE) and Docker containerization. |
| • Extensive hands-on experience with Terraform and AWS multi-account architectures. |
| • Strong scripting abilities in Python or Bash for operational automation. |
| • Solid background in Linux internals, networking, and SSL/TLS security. |
| """ |
| ), |
| JobPosting( |
| id="job-data-platform", |
| title="Data Platform & Analytics Engineer", |
| company="InsightData Corp", |
| location="Berlin, Germany / Remote", |
| type="Remote", |
| salary_range="€75,000 - €95,000", |
| required_skills=["python", "sql", "spark", "kafka", "postgresql", "airflow", "docker", "aws", "pandas", "data engineering"], |
| jd_text="""Job Title: Data Platform & Analytics Engineer |
| Location: Berlin, Germany (Remote across EU) |
| Company: InsightData Analytics |
| Salary: €75,000 - €95,000 |
| |
| About the Role: |
| InsightData is seeking a Data Platform Engineer to design and optimize our batch and real-time data pipelines. You will work with petabyte-scale data lakes and power analytics dashboards for Fortune 500 customers. |
| |
| Key Responsibilities: |
| • Build distributed data ingestion pipelines using Apache Spark, Kafka, and Python. |
| • Orchestrate complex data workflows and ETL transformations using Apache Airflow. |
| • Optimize SQL queries, data warehousing in Snowflake/BigQuery, and PostgreSQL storage. |
| • Collaborate with ML engineers to build reliable feature stores and data APIs. |
| |
| Requirements: |
| • 3+ years experience in Data Engineering or Backend Data Platform development. |
| • Strong proficiency in Python, Advanced SQL, and PySpark. |
| • Experience with streaming and batch systems (Kafka, Spark, Airflow). |
| • Familiarity with Docker, cloud storage (AWS S3), and data quality validation. |
| """ |
| ) |
| ] |
|
|
