How to hire Artificial Intelligence engineers?

From facial recognition software to Big Data analytics, artificial intelligence and machine learning algorithms are making waves in the digital world. An artificial intelligence (AI) engineer can help you build your own AIs and machine learning algorithms.

So how do you hire AI engineers? What follows are some tips for finding top AI engineers on Globelancer.

Unlike robotic process automation (RPA), which automates routine and repetitive structured work, artificial intelligence (AI) represents the next natural evolution in automation, where computer systems can perform tasks that normally require human intelligence such as intuition, judgement, creativity, persuasion and problem solving. This includes but is not limited to cognitive computing, machine learning, computer vision, deep learning and natural language processing. Such technologies have the potential to improve productivity, ease decision making and interactions, as well as enable a new field of innovative services. While AI is still nascent, some outsourcing and shared services (OSS) players are already exploring its potential by enhancing RPA with cognitive capabilities, enabling processing and analysis of unstructured data (e.g. text, voice, images, handwriting). Of the Deloitte Global Outsourcing Survey 2018 respondents using RPA, about one-third are also implementing cognitive automation, while another 59% plan to do so in the next 18 months. Another key use case is the emergence of chatbots, programs that mimic conversations with people using AI techniques. Chatbots can automate a variety of business processes from technology helpdesks, customer contact centers, HR recruitment, to procurement and compliance checking.

Adoption of chatbot technology is fast growing not only among outsourcing and shared services buyers and providers but also across various industries, predominantly in customer service centers, enabling an enhanced user experience with operational benefits (e.g. 24×7 accessibility, high handling capacity, low maintenance costs and faster turnaround). A good example of this can be seen in Autodesk’s adoption and usage of chatbots for handling customer requests, which yielded transformative time and cost savings at scale. Such transformative benefits will in turn accelerate adoption and developments in AI-driven automation. The pace and extent of this is illustrated in one survey, which found 67% of industry professionals reportedly expecting chatbots to outperform mobile apps in the next 5 years. 9 As such, some analysts expect the global chatbot market to grow by 37% per annum in the next 3-5 years, 10 generating potential time savings of over four minutes per enquiry and a predicted US$ 8 billion in cost savings for business per year by 2022

A.I. In Law

Law firms are deploying Natural Language Processing (NLP) and document automation capabilities to fall in line with the reinvention of the legal industry.

Software equipped with AI capabilities is garnering utmost importance among lawyers and firms to save their time.

Document production and proofreading of documents are efficiently done with the help of automation software. Use of team collaboration software is further streamlining the execution of all documentation processes.

All such technological developments have greatly aided lawyers by decreasing costs and increasing convenience.

Case Study – Autodesk uses a cognitive chat-bot for handling customer requests.

Challenge

Autodesk transitioned from a perpetual license to a subscription-based model which increased the customer support required to respond to queries. This resulted in:

• Long wait times

• Higher average resolution time (1.5-2 days)

• Lower customer satisfaction score

Solution

They developed and implemented a chat-bot named ‘AVA’ to handle common customer inquiries such as address changes, login issues, payment issues, and other frequently asked questions.

Impact

The chat-bot helped in improving operational performance of customer support functions demonstrated through:

• A 99% improvement in response times: cutting, resolution from 38 hours to 5.4 minutes for most Tier 1 inquiries.

• A drop in per-query cost from US$ 15 – US$ 200 (human agents) to US$ 1 (virtual agents)

How to shortlist AI engineering professionals

As you’re browsing available AI engineering consultants, it can be helpful to develop a shortlist of the freelancers you may want to interview. You can screen profiles on criteria such as:

• Technology fit. You want an AI engineer who understands the libraries, frameworks, and other technologies used in your technology stack.
• Project experience. You can screen candidates based on their experience working on projects similar to your own.
• Feedback Check reviews from past clients for glowing testimonials or red flags that can tell you what it’s like to work with a particular AI engineer.

How to write an effective AI engineering job post

With a clear picture of your ideal AI engineer in mind, it’s time to write that job post. Although you don’t need a full job description as you would when hiring an employee, aim to provide enough detail for a contractor to know if they’re the right fit for the project.

An effective AI engineer job post should include:

• Scope of work: From deep learning AIs to NLP-powered chatbots, list all the deliverables you’ll need.
• Project length: Your job post should indicate whether this is a smaller or larger project.
• Background: If you prefer experience working with certain industries, software, or technologies, mention this here.
• Budget: Set a budget and note your preference for hourly rates vs. fixed-price contracts.

Ready to bring your AI project to life?