Career opportunities in Conversational AI Enterprises

All of you who have stumbled on this article via your social feeds, I’m Vishwanath Jha, CEO of Saarthi.ai, an omni-channel multilingual conversational ai enterprise that offers pre-trained call center ai agents on-demand to the large enterprises and augment call center productivity.

Okay, so let me make it interactive read and ask you a few questions starting with “Have you ever interacted with customer support agents in the call centers?”. I’d like to believe that the most of you are nodding affirmatively right now and thus comes the follow up question, i.e. “Did you ever feel like why do I’ve to go through a confusing IVR menu and also, why does it take forever to provide resolutions for far simpler issues?” If your answer is “Yes” again, then this blog is definitely worth your next 5 minutes.

All of you who are reading this blog are accessing it via your smartphone, laptops or personal computers and that makes me more confident that you must have had an experience of interacting with ai assistants such as Siri, Cortana, Google Assistant or Alexa, etc. at least once before. Now, imagine having a similar conversational ai assistant for every business app you use today on your phone to assist you along the journey right from the onboarding, discovery, decision making, payment to post-sale support related queries. Isn’t it exciting?

Have you ever wondered how these ai assistants are developed or who are the people involved in engineering such software? Well then, let’s not wait further and deep dive into the world of conversations and the “ai” in it.

Today, conversational ai assistants are not just limited to smartphones, they’ve pervaded our universe across dimensions of channels, domains and verticals. After all, who doesn’t love an extra bit of assistance and specially in a world where people are getting connected to each other faster than ever. We find them everywhere. Here, you must be thinking what am I leading to? Where I want to draw your attention is towards the need for immense numbers of Developers, Conversation Analysts, NLU Engineers, Conversational Experience Designers, Speech & NLP Scientists, Data Annotators, etc.

I thought of putting up a brief introduction to the different career opportunities available in Conversational AI for you.

1. Data Associate/Annotator : Data associates are the most fundamental unit of a conversational ai enterprise. These are the folks who actually help create a meaningful corpus to run experiments using various deep learning architectures and frameworks. Essentially, they are given various plots to think over and then, write several stories based on the same. It requires good command over various languages, ably demonstrated by written & verbal communication skill, sharp observation and logical acumen. It is a very critical function in any ai enterprise as biased data can lead to a huge loss to the organization both financially and scientifically. Hence, quality assessment of the data corpus is also an integral part of the role.
2. Dialog Designer : Great dialog design in a conversational ai assistant often leads to delightful customer experiences. Designing an appropriate persona of conversational ai assistant based upon the business nature and brand profile is an important milestone for any enterprise experimenting with chatbots or voice-bots. Ability to walk into the shoes of different users under various type of scenarios and to put the possible conversations into an architecture design are key traits of the conversation experience designers, also known as Dialog Experience Designers, Conversation Designers, etc. As a dialog designer you would be expected to test conversational flows of the assistant and suggest areas of improvement to the NLU engineer and Chatbot developers.
3. NLU Engineer : NLU stands for Natural Language Understanding and as the name suggests, it refers to the ability of an ai assistant to understand spoken languages that allows it to have conversations with humans about a pre-defined set of topics/contexts in a domain. NLU engineers develop NLU systems aligned with the design in the dialog architecture using various conversational ai frameworks. They must have strong hands- on experience of building NLP applications using deep learning frameworks in python. This is a very popular area of research among the deep learning researchers across leading universities and technology giants.
4. Bot Developer/Chatbot Developer : Due to the growing adoption of ai assistants today, experienced bot developers are highly sought after in the industry. However, there is still a huge gap between the demand for skilled chatbot developers and the supply of pre- trained good quality developers. Thus, it can be stated undeniably that the role of conversational ai developer is a very lucrative career option today as well as in the coming years. If you’ve got a strong problem-solving ability and interpersonal skills to collaborate with diverse teams, you may be the right fit for the role. Of course, there are some technical skills that you’d need to acquire such as solid command over OOPS, python programming, data structure and algorithms, OS, version control tools and other strong engineering concepts along with some practical experience of working with conversational ai frameworks. Additionally, exposure to building web applications would be highly noteworthy.
5. Research Scientist (NLP, Speech Recognition, Dialog Agents) : Conversational AI is not a novel field of study as the quest for the perfect conversational ai system started in the mid-twentieth century. However, it gained significant momentum in the last four to five years due to the paradigm shift in architectures of the computational systems, artificial intelligence algorithms and availability of user generated data on large-scale. Today there are multiple open problems in Conversational AI Research. Leading university research groups as well as most of the leading internet technology companies are actively pursuing these research challenges across languages and dialects. Challenging research problems exist at the intersection of speech & languages and they may range from something such as accurate transcription of audio in spoken languages; synthesizing human like speech from text; speech enhancement; emotion detection; threat, hate and abuse detection; task-oriented dialog frameworks, etc. Research Scientist roles often require either master’s or PhD degree in streams related to computer science, NLP, Speech signal processing, mathematics, etc. Fresh engineering graduates can enter into two-year research fellowship role and get introduction to research methodologies, problem statements, hands-on practical experience, etc. and publish cutting-edge research papers.
6. Conversation/Product Analyst : Analytics is important to the growth and maturity of any business or product. Conversation analyst is the role where you’re expected to generate actionable business insights regarding the current performance of conversational ai assistant as well as possible tweaks and feature additions for future. As a Conversational Product Analyst, you’d be required to have good command in analyzing historical conversations using deep learning frameworks in statistical software such as R/Python etc. An important and interesting part of the job would be to identify novel conversational constructs across unique nature of conversations and their semantic representations and collaborate with the Research Scientists and Chatbot Developers to explore the validity as well as feasibility of the same.

I hope that I was able to deliver enough value for the time you spent reading this piece. If you were able to grasp a good understanding of the different roles that comprises a conversational ai team, the purpose of this blog would be deemed served. Please feel free to share your comments and feedbacks as well as don’t forget to share this on your timelines across channels for better reach and larger good. Thank you for your time and patience.