The progress is outpacing our ability to understand it.

We may soon all live in a matrix-type scenario, living as batteries to support the robot infrastructure.

That’s why I reached out to Manthila Baduraliyage to save us!

Manthila is working with a major Japanese IT consultancy that is building a team to solve client problems with generative AI products and services.

This team is still in its infancy, but it was such a unique opportunity that I asked her for every detail she has about this role.

In this guide we cover what it is, the day-to-day tasks, the requirements, how to get a job, and FAQs about the position.

There’s a lot to cover, so let’s get started.

What is a Generative AI Consultant?

As a Generative AI Consultant, you will prototype generative AI systems for internal use that will become services offered to clients.

The generative AI systems at this company are in their early stages. These systems generate new data based on their training, and you will play a crucial role in designing models for specific tasks.

The focus is on Large Language Model (LLM) generative AI, using technology for text interpretation and generation, and integrating it into in-house systems.

Responsibilities for the Generative AI IT Consultant Engineer

As a Generative AI IT Consultant Engineer, your multifaceted role will integrate aspects of software engineering, AI technology, and business strategy.

Here’s an overview of your responsibilities:

Designing and Developing User-Friendly Business Functions

Your task will be to create effective and intuitive business solutions. This involves making complex technical processes accessible and user-friendly for end-users in large companies.
Continuously enhancing interface design will be crucial to ensure that applications are easy to navigate, even for non-technical users.

Collaborating with the Business Side

Engaging closely with business teams is essential to understand their needs and challenges. Your proactive and assertive collaboration will ensure that tech solutions align with business strategies.
This role involves bridging the gap between technical possibilities and business necessities, making sure that solutions are practical and relevant.

Researching LLM Applications and Technologies

Keeping updated with the latest in Large Language Models (LLMs) and related technologies is a key aspect of your role. This includes both academic research and practical applications.
You will be expected to stay at the forefront of LLM trends, exploring new methods and determining their applicability to your organization's systems and practices.

Leading Product Development Teams

If you have experience as a tech lead or full stack engineer, you will be expected to guide and manage teams throughout the product development lifecycle.
This involves ensuring that projects are executed efficiently, meeting quality, time, and budget constraints.

Utilizing Open-Source LLM

Working with open-source LLMs offers a unique opportunity for experimentation and innovation with different tools and platforms.
You will be required to stay connected with community developments and best practices in open-source projects.

Engaging in Dynamic and Fast-Paced Development

Your role will involve rapid development and iteration of new ideas and solutions, contributing to your organization's evolving business structure.
You will play a crucial role in organizational change, with a focus on transforming work processes and practices.

Prototyping and Infrastructure Improvement

Developing new services through prototyping includes testing and refining ideas in low-risk settings before full-scale implementation.
You will also be responsible for establishing and improving the infrastructure necessary to support these services.

Direct Impact and Feedback

The opportunity to receive immediate feedback on your work will provide direct insights into its impact and effectiveness.
This feedback loop enables rapid refinement and adjustment of services, contributing to a swift release cycle.

Cross-Departmental Involvement

Your role will extend beyond technical tasks to involve working across different departments, leveraging the collective resources and capabilities of the organization.
Participating in the review and development of specifications ensures alignment with broader organizational goals and strategies.

Now, let's take a look at what you will be doing every day of your life (except on weekends and holidays):

Day-to-Day Tasks

As a Generative AI IT Consultant Engineer, your daily routine will be diverse and dynamic, involving various critical tasks that contribute to the development and maintenance of generative AI systems. Here's a more detailed look:

Morning Routine

Start with a Team Meeting: Begin your day with a brief team meeting. This is a time to align on daily goals, discuss any challenges, and update on the progress of ongoing projects.
Review Emails and Communications: Check your emails and other communication channels for any important updates from clients, management, or team members that may impact your day's work.
Plan Your Day: Prioritize your tasks based on the discussions in the team meeting and any new information from your emails.

Mid-Morning to Afternoon

Development Work: Dive into the core of your day, which involves developing new services or improving existing ones. This could include writing code in C++ or Python, working on system design, or troubleshooting and debugging.
Prototyping and Testing: Spend time on prototyping new ideas. This involves creating low-risk, small-scale versions of new features or services, testing them, and gathering initial feedback.
LLM Research and Application: Allocate time to stay updated on the latest in LLM applications and technologies. This might involve reading recent publications, experimenting with new LLM features, or applying new learnings to your current projects.

Afternoon Routine

Collaboration with Other Departments: Engage with business teams or other departments. This might involve discussing how the AI systems can be integrated into different parts of the business, or how they can be tailored to better meet business needs.
User-Centric Design and Development: Focus on designing and developing business functions. Ensure they are user-friendly and meet the requirements of large corporate clients.
Documentation and Reporting: Document your progress and any new findings or challenges. Prepare brief reports or updates to share with your team or superiors, as required.

End of Day

Wrap-Up and Preparation for the Next Day: Review your accomplishments for the day. Prepare a list of tasks or goals for the next day, ensuring a productive start in the morning.
Team Debrief: If your team practices end-of-day debriefs, participate in these to share progress and highlight any critical issues or successes from the day.
Professional Development: If time allows, dedicate some time to personal learning and professional growth. This might include learning a new programming technique, exploring advanced features of LLM, or even language improvement if you're working on enhancing your Japanese skills.

Weekly or Periodic Tasks

Team Strategy Meetings: Participate in broader team strategy meetings (perhaps weekly) to discuss long-term goals, project roadmaps, and departmental updates.
Client Meetings and Negotiations: Depending on your role, you may have periodic meetings with clients or stakeholders to discuss project updates, gather requirements, or negotiate details of the services.

What are the requirements to make this your life?

Requirements

Backend Engineering Experience: Minimum of 3 years in backend engineering, especially with C++ or Python. The role requires someone who can confidently maintain and develop generative AI systems.
Service Development and Operation: Experience in planning, developing, and operating services, which includes designing and developing new functions and modifying existing ones.
Cloud Environment Expertise: Hands-on development and operation experience in a cloud environment, indicating familiarity with modern cloud-based infrastructures.
Leadership Abilities: Capability to lead a product development team based on your technical background.
Development Process Knowledge: Understanding of the development process, standardization, and optimization of the development environment.
Technical Decision-Making: Ability to make informed decisions regarding architecture and framework selection.

Bonus Points

LLM Application Development: Experience in developing applications using LLM, particularly with tools like OpenAI and LangChain.
Natural Language Processing: Knowledge in NLP suggests a deeper understanding of the intricacies of language models.
Leadership Experience: Past roles as a tech lead, showing the ability to guide teams and projects.
Client Interaction Skills: Experience in negotiating with customers and product owners, an important aspect for client-facing roles.
Team Output Improvement: A history of enhancing team output, whether through code, design, or production process improvements.
Agile Development Experience: Familiarity with agile methodologies, like Scrum, illustrating adaptability and efficiency in development processes.

Here are the 5 skills that Manthila said you should focus on to get a job as a Generative AI Consultant.

5 Skills That Will Make You Stand Out

LLM (Large Language Model) or NLP Experience
- Why It's Important: Knowledge in LLM or NLP positions you to better understand and develop AI-driven language models, which are pivotal in generative AI.
- How to Build It: Engage in courses or projects focused on NLP, participate in AI conferences, and contribute to open-source NLP projects.
Python or C++
- Why It's Important: Proficiency in these languages is essential for backend development, enabling you to build and maintain robust AI systems.
- How to Build It: Practice through coding challenges, contribute to open-source projects, and stay updated with the latest language developments.
Japanese Skills
- Why It's Important: Proficiency in Japanese is crucial for effective communication within the Japanese tech industry.
- How to Build It: Take language courses, immerse yourself in Japanese culture and media, and practice speaking with native speakers.
Machine Learning Experience
- Why It's Important: Understanding machine learning is key to developing intelligent AI systems and improving their efficiency and effectiveness.
- How to Build It: Participate in machine learning bootcamps, work on machine learning projects, and stay abreast of industry trends and innovations.
Education in AI or Language Models
- Why It's Important: A strong educational background provides a foundational understanding of AI principles and methodologies.
- How to Build It: Pursue relevant degrees or certifications, attend workshops and seminars, and engage in continuous learning.

What is the career path for a Generative AI Consultant?

Career Progression

The career path in this role is dynamic and dependent on product success and company growth. Opportunities for career expansion are abundant, including leadership roles in product development, R&D, customer support, business development, and organizational management.

Entry Level

Starting Point: You begin as a Backend Engineer, honing your skills in C++ and Python, and getting accustomed to working with generative AI systems.
Focus: Gaining practical experience in maintaining and developing AI systems and understanding the basics of LLM and NLP.

Mid-Level

Position: AI System Developer or LLM Specialist.
Responsibilities: Taking on more complex projects, starting to lead smaller teams, and diving deeper into generative AI technologies.
Growth Opportunities: Potential to become a Tech Lead or a Project Manager, overseeing significant projects or leading innovation in specific AI areas.

Senior Level

Role: Senior AI Consultant or Project Lead.
Scope: Leading large-scale projects, driving innovation, and possibly getting involved in strategic decision-making processes.
Advancement: Opportunities to move into upper management, such as becoming a Department Head or a Director of Technology.

Executive Level

Position: CTO or Head of AI Innovation.
Leadership: Shaping the company's technological direction, mentoring upcoming leaders, and being involved in high-level strategic planning.
Expansion: Venturing into business development, organizational management, or even starting your own consultancy in the field of generative AI.

Ready to apply? Use this link to reach out to us!

Before you do, here are some tips from Manthila to help you land the job.

Tips for Landing the Job

Be Honest and Direct: It's crucial to be upfront about your experience, your expectations from the company, and what you can realistically provide. Given the newness of the role, clarity and honesty are especially important.
Showcase Your Experience: Emphasize your backend engineering experience, particularly in C++ or Python. Highlight any relevant projects or roles that demonstrate your ability to maintain and develop AI systems.
Express Your Expectations Clearly: Make sure to communicate what you expect from the company and the role. This helps ensure alignment between your career goals and the company’s objectives.
Understand the Role's Novelty: Acknowledge the innovative nature of the position and show enthusiasm for working in a new and developing field.

Interview Tips

Prepare for Two Interview Rounds: The first with a lead engineer and the second with a site manager. Familiarize yourself with the job requirements and how your skills align with them.
Understand the Role and Company: Research the company and the specifics of the Generative AI IT Consultant Engineer role. Knowing the company’s products, culture, and goals can help you tailor your responses to align with their vision.
Technical Proficiency Demonstration: Be prepared for a live coding session. This is your chance to showcase your technical skills, especially in C++ and Python. Practicing common coding problems can be beneficial.
Language Proficiency: If you're non-native in Japanese, be prepared to demonstrate your proficiency if required. While English may not be a prerequisite, clear communication skills are vital.
Answer Directly and Clearly: During the interview, respond to questions straightforwardly. Avoid deviating from the topic. If a question seems unclear, it's okay to ask for clarification.
Exhibit Problem-Solving Skills: Be ready to discuss how you approach problem-solving, particularly in a technical context. Examples from past experiences can be very impactful.
Ask Insightful Questions: Show your engagement and interest in the role by asking thoughtful questions about the company, the team you'll be working with, and the specifics of your daily tasks.
Showcase Your Soft Skills: While technical skills are paramount, don't forget to highlight your teamwork, communication, and leadership abilities, which are crucial in collaborative environments.

Let’s go over the reasons Manthila has seen people get rejected.

Reasons for Rejection

Research Over Development: Candidates who have a strong research background but lack practical development experience, especially in backend engineering, may not meet the hands-on requirements of the role.
Insufficient Backend Engineering Skills: Strong skills in backend development, particularly in C++ and Python, are crucial. Candidates who fail to demonstrate sufficient proficiency in these areas are often not considered.
Inadequate Language Skills: While English proficiency may not be a requirement, insufficient language skills, particularly in Japanese, can be a barrier, considering the need for effective communication within the team and with clients.
Failure to Answer Questions Effectively: Candidates who struggle to provide clear, direct answers to interview questions, or those who fail to demonstrate a good understanding of the role and its requirements, may not be successful.
Lack of Practical Experience in LLM: Candidates without practical experience in Large Language Models or NLP might find it challenging to handle the specific demands of this role.
Weak Problem-Solving Skills: Inability to showcase strong problem-solving skills, especially in a technical context, can lead to rejection.
Inadequate Preparation for the Live Coding Session: Failing to perform well in the live coding session, which assesses technical proficiency, can be a significant reason for not moving forward in the interview process.
Lack of Alignment with Company Culture: Failing to demonstrate how your personal and professional values align with those of the company can also be a deciding factor.

FAQ

Is English Proficiency Required?
- No, English proficiency is not a prerequisite for this role. However, clear communication skills are essential.
Why Does the Company Need a Generative AI Employee?
- The company is focused on developing internal products that leverage generative AI technologies, specifically for enhancing efficiency and productivity.
Are There Other Team Members in This Role?
- Yes, you will be part of a team. Collaboration and teamwork are key aspects of this role, as you'll work alongside other engineers and departments.
What Kind of Products Will You Be Working On?
- The products are primarily for internal use, aimed at improving business processes and efficiencies within the company. The specifics may vary but involve generative AI applications.
What Is the Potential for Career Advancement Within the Company?
- The potential for advancement is significant. Depending on the company's growth and the success of its products, you could move into roles such as development lead, R&D manager, or even higher management positions.
What Type of Company Culture Can I Expect?
- The company culture is likely to be dynamic and innovative, with a focus on continuous learning and adaptation to new technologies in the AI space.
How Important Are Backend Engineering Skills for This Role?
- Backend engineering skills, especially in C++ and Python, are crucial. They form the foundation for developing and maintaining the generative AI systems you'll be working with.
Is There a Specific Educational Background That Is Preferred?
- While a specific educational background in AI, machine learning, or computer science is highly beneficial, practical experience and demonstrated skills in relevant areas are equally important.
What Challenges Can I Expect in This Role?
- Challenges may include staying abreast of rapidly evolving AI technologies, integrating generative AI into existing systems, and collaborating effectively with various teams within the company.
How can I apply for the Generative AI Consultant position?
- Message us using this link to learn more about the position!