AI ML Engineer

AI ML Engineer

AI / ML Engineer

AI & Data Science Bootcamp · Atlanta, GA · Full-Time W2 Employment

The models shaping every industry are being built by engineers like you. In 9 weeks, go from data-aware developer to a production-ready AI/ML Engineer — paid from day one, trained on real tools, and deployed on Fortune 1000 client projects.

Who this is for

You already write code. You understand data. Python and SQL are tools you use daily — not things you read about. What you haven't had yet is the structured path to go from writing scripts and running analyses to engineering the machine learning systems, AI pipelines, and predictive models that power real enterprise decisions.

This program is built for IT professionals who:

• Have 1+ year of professional coding experience — Python used regularly as a core part of their role
• Understand data at a technical level: SQL queries, data structures, APIs, and working with raw datasets
• Have a foundation in statistics and math — probability, distributions, and basic linear algebra
• Write structured, maintainable code with OOP principles — not just notebook cells
• Are curious about AI and ML and have taken at least some steps to explore it independently
• Are willing to relocate to Atlanta, GA for training and to client sites for project assignments

Education: A master's degree in CS, Engineering, Mathematics, Statistics, or a related quantitative field is preferred.

What you get

From day one of training, you are a full-time W2 employee. Not a student. Not an intern. An AI/ML Engineer.

Full-time W2 salary from day one - Health, dental & vision insurance

Corporate housing & relocation covered - 401(k) eligibility after one year

Fortune 1000 client exposure - Dedicated support team & mentorship

Performance bonuses - 25+ years of placement expertise behind you

What you will learn in 9 weeks

Core AI & machine learning

Supervised & unsupervised learning — regression, classification, clustering

Model training, evaluation, and tuning with scikit-learn, PyTorch & TensorFlow

Feature engineering, cross-validation, and pipeline design

NLP, computer vision, and generative AI fundamentals

LLMs, prompt engineering, and Retrieval-Augmented Generation (RAG)

MLOps — model deployment, monitoring, versioning, and CI/CD

Data engineering & cloud platforms

PySpark, Databricks, Airflow, and big data pipeline design

AWS (S3, Lambda, EC2, SageMaker), Azure ML, and GCP Vertex AI

SQL at scale — advanced queries, window functions, and data warehousing

Docker, Git, GitLab, and enterprise dev workflows

Data security, GDPR, and compliance best practices

AI-assisted development with GitHub Copilot & Claude AI

Program timeline

Weeks 1–2 - Python, SQL, and data engineering foundations — pipelines, APIs, statistics, and OOP at scale

Weeks 3–4 - Core ML — model training, evaluation, feature engineering, scikit-learn, and cloud data platforms

Weeks 5–6 - Advanced AI — deep learning, NLP, computer vision, and generative AI with PyTorch & TensorFlow

Weeks 7–8 - LLMs, RAG, MLOps, Databricks, PySpark, and end-to-end deployment on AWS / Azure / GCP

Week 9 - Client readiness — interview prep, professional skills, and placement support

Required skills & qualifications

We review every resume carefully. Here is exactly what we look for — the more clearly your resume demonstrates these skills, the faster you will move through our process.

Must-have — non-negotiable

• Python: 1+ year of Python as a core part of your job — data analysis, automation, pipelines, or ML experimentation
• SQL: Proficient SQL — JOINs, aggregations, subqueries, and working with real databases or data warehouses
• Stats: Statistics foundation — probability, distributions, hypothesis testing, and understanding what data reveals
• Data: Hands-on data wrangling — cleaning, transforming, and handling messy real-world datasets in code
• Git: Git version control in a team setting — branching, pull requests, and collaborative code workflows
• APIs: REST API experience in Python — calling endpoints, parsing JSON, handling authentication
• OOP: OOP fundamentals — classes, inheritance, and interfaces used in real production or analysis code
• Location: Willingness to relocate to Atlanta, GA for training and travel to client sites for placements

Highly preferred — fast-tracks your application

• Any ML library experience: scikit-learn, TensorFlow, PyTorch, or Keras — even personal or academic projects
• Cloud platform exposure: AWS (S3, Lambda, EC2, SageMaker), Azure ML, Databricks, or GCP
• Big data tooling: PySpark, Airflow, Spark, or distributed computing environments
• Generative AI or LLM exposure: OpenAI API, LangChain, Hugging Face, or RAG pipeline experience
• Data visualization skills: Matplotlib, Seaborn, Plotly, Tableau, or Power BI
• Active GitHub or Kaggle profile with Python notebooks, ML projects, or competition history
• Familiarity with R, Scala, or additional statistical computing languages

Education

• Master's degree in Computer Science, Data Science, Software Engineering, Mathematics, Statistics, or a related quantitative field is preferred

Why this opportunity stands out

Every company in the world is trying to hire AI/ML Engineers. Most can't find them fast enough.

Most data professionals spend years picking up ML skills between projects, online courses, and side experiments. This program compresses that into 9 weeks of intensive, structured training — covering Python, statistics, deep learning, LLMs, cloud deployment, and MLOps — then places you directly on paid client engagements with Fortune 1000 companies across finance, healthcare, retail, e-commerce, and technology.

You will leave with:

• Real enterprise AI/ML project experience — not toy datasets or sandbox environments
• End-to-end ML pipeline skills most engineers are still slowly self-teaching
• Hands-on LLM and generative AI experience that is in extremely short supply right now
• Verifiable Fortune 1000 consulting experience on your resume
• A peer network of engineers who went through the same intensive program
• A clear, supported pathway to senior-level AI/ML placement

Our clients include:

Microsoft · Google · PayPal · Walmart · T-Mobile · Wells Fargo · Capital One · Johnson & Johnson · Nike · Dell · CVS · Verizon · McDonald's · Charles Schwab · Fannie Mae · Charter

Ready to apply?

Submit your resume and complete our AI-powered interview for the fastest consideration. We review applications on a rolling basis — cohort spots are limited and fill quickly.

Strong English communication skills (written and verbal) are required for all client-facing roles.

Apply today — the models that move industries need engineers like you.