Who are Peterson Technology Partners?

Peterson Technology Partners, also known as PTP, is a staffing and consultancy firm, located in Chicago and operating globally. PTP specializes in providing IT staffing and talent supply management solutions.

What services does PTP offer?

PTP is known for sourcing best-in-class talent for the IT industry, specializing in the fields of Artificial Intelligence, Data Science, Cloud Computing, DevOps, and Cybersecurity.

What are the benefits of working with PTP?

PTP’s proprietary 5-step assessment process ensures that our clients receive only the absolute best-in-class talent for their staffing needs. Our 4-point promise to every client guarantees Quality, Speed, Agility, and Customization.

Do I have to pay for this service?

We require no payment from job-seekers. If you’re a job-seeker looking for your next great opportunity visit the Jobs page. If you’re an employer looking for top-tier talent to meet your staffing needs, Contact Us to start building your customized service plan.

Jobs in AI/ML - Hiring Artificial Intelligence & Machine Learning Engineers

What Is Machine Learning (ML) and Artificial Intelligence (AI)?

The wealth of data that is being generated by consumers today have made Machine Learning (ML) and Artificial Intelligence (AI) key scientific tools for modern business.

AI is a branch of computer science which focuses on building intelligent machines which can use deep learning and natural language processing to automate functions that would otherwise require human beings.

ML is a subset of AI that uses large amounts of data and algorithmic programming to learn and perform tasks independent or semi-independent of human supervision.

AI is employed in many different industries like from healthcare to transportation. Chatbots, AI recruiting platforms, e-commerce and retail assistants—many day-to-day customers service operations can be automated and performed with ML and AI.

Why is AI/ML important?

AI and ML applications provide incredible opportunities — both in cost savings and revenue growth, depending on the use case. Organizations that have implemented machine learning algorithms in the last three to five years see a return on investment today. If your enterprise has not included AI/ML in your business strategy, it is time to consider focusing on it to remain competitive in the modern marketplace.

Increase your performance
Reduce operational costs

Increase your performance
AI-related technologies — such as ML, deep learning and natural language processing (NLP) — can be used to produce reliable, relevant, dynamic and intelligent information that can help with decision making. They also offer backend integrations with APIs with their case management or ERP systems.
Reduce operational costs
The increased adoption of AI can be attributed to automating manual or statistics-driven tasks that save time on repetitive tasks and increases focus on high-value jobs. Machine learning can lead to cost decreases associated with optimization of inventory or pricing, contact-center automation and claims processing.

What skills do you need to work in AI/ML?

These are some of the key skills you need to take advantage of growing opportunities in the AI/ML field and find success:

To become an expert in machine learning it’s important to grow your experience with programming languages. Python, C++, JavaScript, Java, and Scala are some of the most common programming languages used in AI/ML coding.
The first step in machine learning development is pre-processing and storing raw data generated by your systems.
Being able to perform exploratory data analysis on a dataset is an especially important skill because it allows you to discover interesting patterns in data, identify certain anomalies and test hypotheses.
If you want to be a pro in machine learning, you need to be proficient in machine learning algorithms. But that’s not enough; you also need to know when to apply them. For doing more complex tasks such as image classification, object detection, face recognition, machine translation, dialogue generation, etc., you will need more complex algorithms that fall in the deep learning category, which is based on artificial neural networks.
As in every software solution, managing security for machine learning solutions is a crucial task, especially because machine learning models need a lot of data to be trained, and access to that data should be provided to authorized people and applications only.

Build a career in Machine Learning (ML) and Artificial Intelligence (AI)

Looking to start or build on a career in AI/ML?
Head over to our jobs page to find your next great opportunity!

Apply Now

Frequently Asked Questions

Is AI/ML a good career choice

Yes, AI/ML is a good career path. According to a recent report, Machine Learning Engineer is one of the top jobs in the United States in terms of salary, growth of postings, and general demand.
What are some of the most common roles in AI/ML?
There are many career paths artificial intelligence and machine learning professionals can choose to take within the industry.

The top 5 Artificial Intelligence Jobs in the US are:
- Machine Learning Engineer
- Data Scientist
- Business Intelligence Developer
- Research Scientist
- Big Data Engineer/Architect.
What is the difference between AI, Machine Learning and Deep Learning?

Artificial Intelligence, Machine Learning, and occasionally, Deep Learning are closely related concepts that are sometimes used interchangeably when referring to the building blocks of automation.

Artificial Intelligence:
Artificial intelligence is a very broad term covering many facets of science, engineering, computing, and more. In its simplest form, artificial intelligence refers to “the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence”, according to the former Dean of Computer Science at Carnegie Mellon, Andrew Moore.

Machine Learning:
Machine learning (ML) is a type of artificial intelligence programming that is focused on building applications that can use data to learn and become more accurate over time, without additional programming. To put it another way, machine learning is the science of getting computers to process data, make assumptions and act on them, without explicitly being told what to do.

Deep Learning:
Deep Learning is a subset of machine learning that can be considered an advancement in the field. As IBM puts it, all deep learning is machine learning but not all machine learning is deep learning.

Deep learning is about using multiple layers of analysis to extract higher levels of understanding from data and uses what is known as an artificial neural network (ANN) to help computers process information the way a human brain would.