AI Product Lead

About the Company

MANGO OFFICE has been a leader in the Russian cloud communications market since 2001 and is among the top 20 largest IT developers in Russia.

The company employs over 1000 people, with branches in 18 cities. MANGO OFFICE products are used by Skyeng, Citimobil, VkusVill, AlfaStrakhovanie, SberMarket, and other companies.

We are developing a business communications platform for sales, support, and contact centers. The MANGO OFFICE ecosystem includes a virtual PBX, an omnichannel contact center, speech analytics, robots, messengers, integrations, and customer interaction management tools.

AI is now becoming one of the key development areas for MANGO OFFICE. We are looking for an AI Product Lead who will help transform individual AI initiatives into a working system: identify strong business scenarios, quickly test hypotheses, and bring successful solutions to industrial implementation and measurable results.

Why This Role Exists

MANGO OFFICE already uses AI tools and has initiatives emerging within product, technical, and business teams. We see great potential for AI in customer service, sales, development, analytics, and internal operational processes.

However, there is currently no single owner responsible for the entire journey:

from problem and hypothesis → to prototype → pilot launch → industrial implementation → confirmed business effect.

We don't want to start with a multi-year strategy, a large platform, or a competence center for its own sake. First, we need working solutions that employees and clients actually use.

Main Task

Make AI an integral part of MANGO OFFICE's product and operational model, not just a collection of separate experiments and demonstrations.

We need a leader who can:

find a valuable process → understand user needs → formulate a hypothesis → quickly build a prototype → test the effect → organize industrial implementation → ensure solution usage and scaling.

In the first 6–9 months, the primary focus of the role will be the automation of internal processes and increasing the efficiency of MANGO OFFICE teams.

Developing customer-facing AI capabilities is also part of the role's scope but will be done in collaboration with the owners of the respective products.

Opportunities and Resources at the Start

This is not a role where you will be working alone, going to different departments, collecting ideas, and waiting for developers to become available.

The AI Product Lead will have:

• a dedicated development team to implement AI initiatives;
• the ability to involve specialists from four internal product train teams;
• access to 1C, CRM, billing, and internal web product teams for integrations and industrial implementation;
• direct interaction with product and functional leaders;
• participation in setting AI direction priorities;
• support from analysts, architects, infrastructure, and information security teams;
• the opportunity to use ready-made AI tools, external platforms, and partner solutions;
• the opportunity to form the core AI team and define its future development model;
• access to real company processes involving over 1000 employees;
• the opportunity to develop AI scenarios both within MANGO OFFICE and in products for corporate clients.

The role will have sufficient resources to move beyond research and presentations to quickly proceed to prototypes and implementations.

What You Will Be Doing

Searching for Strong AI Scenarios

• Studying the processes of business and product departments.
• Identifying manual labor, time losses, errors, bottlenecks, and growth opportunities.
• Conducting interviews and observing user workflows.
• Analyzing processes, data, and operational economics.
• Helping internal stakeholders transform unstructured requests into a clear product specification:
  • problem;
  • user;
  • current process and baseline metrics;
  • hypothesis;
  • first working version of the solution;
  • expected effect;
  • success criteria.
• Distinguishing tasks that truly require AI from those that are more reasonably solved by standard automation.

Quickly Testing Hypotheses

• Personally participating in the creation of the first prototypes for the most promising solutions.
• Using LLM APIs, Cursor, Copilot, Claude, OpenAI, GigaChat, YandexGPT, n8n, Make, and similar tools.
• Independently or with the team, building simple AI agents, RAG scenarios, assistants, integrations, and automation chains.
• Testing not only technical feasibility but also the real value of the solution for the user.
• Evaluating quality, stability, cost, response time, error risks, and operational requirements.

We do not expect the AI Product Lead to write production code every day. However, it is important to be able to independently go from hypothesis to a working prototype and to have substantive discussions with developers, analysts, and architects.

Bringing Solutions to Implementation

• Managing the full lifecycle of AI solutions: from idea to industrial operation.
• Launching pilots with predefined baseline metrics, target metrics, and scaling criteria.
• Collaborating with teams to define architecture, integrations, data requirements, observability, information security, and maintenance.
• Integrating solutions into real systems and workflows.
• Being responsible not only for the launch but also for the actual use of the solution by employees or clients.
• Halting initiatives that do not prove their value.
• Scaling solutions that have demonstrated their effect.

Calculating Business Impact

• Evaluating the impact of AI initiatives on:
  • revenue and financial results;
  • labor costs;
  • process execution speed;
  • cost per operation;
  • service quality;
  • number of errors;
  • conversion rates;
  • customer experience;
  • team productivity.
• When evaluating, consider not only potential benefits but also the total cost of ownership: model usage, infrastructure, integrations, maintenance, and quality control.

Managing the AI Initiatives Portfolio

• Creating a clear process for receiving, critically evaluating, and prioritizing AI requests.
• Maintaining a transparent list of initiatives, where each task includes:
  • problem;
  • owner;
  • priority;
  • status;
  • expected effect;
  • cost;
  • risks;
  • dependencies;
  • next step.
• Developing and prioritizing the list of AI direction tasks.
• Coordinating the work of business, product, development, analytics, information security, architecture, and infrastructure teams.
• Preparing a clear picture for management: progress, achieved results, risks, obstacles, and necessary decisions.

Developing Product AI Capabilities

• Collaborating with product teams to find and test AI scenarios in the areas of:
  • contact center;
  • sales;
  • customer support;
  • speech analytics;
  • communication quality control;
  • operator and manager prompts;
  • AI assistants and AI agents;
  • automated request processing;
  • self-service for clients;
  • identifying patterns and growth opportunities in communications.
• The AI Product Lead does not replace product owners but helps them faster find, test, and scale strong AI capabilities.

Developing a Culture of Practical AI Application

• Forming the core AI team.
• Creating a community of employees who will promote the practical use of AI within product and business departments.
• Helping teams apply AI tools in their daily work.
• Establishing clear guidelines:
  • how to find AI scenarios;
  • how to describe hypotheses;
  • how to build prototypes;
  • how to launch pilots;
  • how to measure impact;
  • how to safely work with corporate data.
• Conducting internal demonstrations, practical case studies, and working meetings with teams.

Our goal is to foster a culture of practical AI application through real results, not through general lectures on the possibilities of artificial intelligence.

Who We Are Looking For

• Minimum of three years of experience as a Product Manager, Product Owner, Product Lead, Head of Digital Products, or Head of Analytics with responsibility for launching solutions.
• Practical experience with AI products, ML/LLM solutions, or business process automation.
• Experience in completing the full cycle: from problem and hypothesis to implementation and measurement of results.
• Ability to understand complex and unstructured business processes.
• Ability to conduct interviews, research user needs, and test product hypotheses.
• Capability to transform business requests into a first working version of a solution, requirements, task list, and measurable results.
• Ability to calculate and defend business impact.
• Experience working with developers, analysts, ML/AI specialists, architects, information security, and business stakeholders.
• Willingness to personally delve into processes, data, and prototypes.
• Independence, systems thinking, and a focus on implementation.

It will be more challenging for us to collaborate if the candidate's primary experience is solely related to AI strategy development, research management, or launching pilots without subsequent industrial implementation.

Technical Expertise

It is important to understand:

• LLM operating principles;
• AI agent and agent scenario architecture;
• RAG;
• vector data representation and vector search;
• designing prompts for language models;
• connecting external tools and invoking functions;
• API integrations;
• REST API, webhooks, and JSON;
• basic AI solution architecture;
• methods for evaluating the quality and stability of results;
• causes of factual errors and hallucinations, and methods to limit them;
• response time and processing costs;
• data, information security, and personal data requirements;
• the difference between a prototype and a stable industrial solution.

Bonus Points

• Experience in B2B SaaS, corporate products, or solutions for large businesses.
• Experience launching a new product or technology direction from scratch.
• Experience in automating internal processes.
• Experience with telephony, contact centers, CRM, speech analytics, sales, or support.
• Experience creating RAG systems, corporate knowledge bases, AI assistants, or AI agents.
• Practical experience with agent development (with real case studies).
• Basic knowledge of Python, SQL, or experience working with APIs.
• Experience preparing materials for strategic committees and management reviews.

Expected Results in the First 6 Months

• Analysis of existing AI initiatives, processes, tools, and competencies completed.
• A map of AI application opportunities within MANGO OFFICE created.
• 10–15 most valuable AI scenarios identified and evaluated.
• A transparent process for receiving and prioritizing initiatives launched.
• Four to six initiatives have progressed to a working prototype or pilot launch.
• Two to three solutions have been launched for regular use.
• Measurable financial or operational impact confirmed for at least one solution.
• Baseline metrics, actual usage, quality, and operational costs measured for implemented solutions.
• The core AI team formed, and an interaction model with business and product departments established.

How Success Will Be Measured

• Number of initiatives that moved from idea to prototype.
• Number of pilots that reached industrial use.
• Proportion of solutions with confirmed business impact.
• Impact on financial results, productivity, process speed, quality, and customer experience.
• Actual usage of implemented solutions.
• Hypothesis testing speed.
• Stability and operational cost of AI systems.
• Transparency of the AI initiatives portfolio.
• The team's ability to regularly find and launch new AI scenarios.

Why This is an Interesting Role

• Opportunity to build an AI direction in a large and mature Russian IT company.
• A dedicated development team is available – you won't be limited to research and presentations.
• Access to specialists from four product teams for integrations and scaling solutions.
• Ability to work simultaneously with internal processes and customer-facing B2B products.
• MANGO OFFICE works with voice, text communications, sales, support, and contact centers – offering a wide range of applied AI scenarios.
• Opportunity to quickly test hypotheses and see results in real business metrics.
• The role offers high autonomy and direct interaction with product, functional, and technical leaders.
• As the direction develops, the AI Product Lead can expand the team, scope of responsibility, and influence within the company.

Work Format

• Full-time employment.
• Work format discussed individually.
• Regular interaction with product teams, business units, development, analytics, architecture, infrastructure, information security, and company management.

Selection Stages

• Application and a brief cover letter.
• Initial interview.
• Meeting with the product lead.
• Expert meeting on AI scenarios and implementation approach.
• Final meeting.
• Job offer.

What to Include in Your Cover Letter

Briefly describe one or two examples of launching an AI product, AI service, or automation:

• what business or process you changed;
• what problem you solved;
• what the original process looked like;
• what solution was launched;
• what tools were used;
• how the solution was integrated into regular operations;
• what measurable impact was achieved;
• how many users utilized the solution;
• what exactly you did personally;
• whether the solution reached industrial deployment.

Applications with specific and verifiable examples will be considered first.

We await your applications via tg @AlinaKharchenkoHR