AI Enablement Lead (GenAI / LLM)

This role combines AI architecture, technical leadership, hands-on development, and strategic planning. You will own the technical direction of AI within the product, from concept to production at scale.

This is not a pure research or data science role. We are looking for an AI Enablement Lead - a technically strong, hands-on professional who can translate AI capabilities into real, adopted solutions.

You will work at the intersection of technology, business processes, people and teams.

• ~60% of the role focuses on enablement, communication, adoption, and standards,
• ~40% on hands-on technical work and solution design.

You will act as a one-person AI owner at the beginning, with the mandate to define how AI is used, governed, measured, and scaled.

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Responsibilities

AI Enablement & Adoption (core part of the role)

• Work closely with non-technical teams to understand how they work today and identify where AI can realistically improve efficiency.
• Introduce and promote practical usage of GenAI tools (e.g. ChatGPT Enterprise, Cursor, internal assistants).
• Explain AI benefits in a clear, non-technical way and build trust around its usage.
• Define standards, rules, and best practices for AI usage across the organization.
• Build and own a knowledge hub (documentation, guidelines, onboarding materials, examples).
• Act as the internal point of contact for AI-related questions and initiatives.

Technical & Engineering Responsibilities

• Design and implement practical GenAI solutions (assistants, automations, internal tools).
• Build PoCs and lightweight prototypes to validate ideas quickly.
• Work hands-on with LLMs, RAG patterns, embeddings, and vector stores where needed.
• Integrate AI solutions with existing backend systems and workflows.
• Support developers in adopting AI tools in their daily work (e.g. coding assistants, workflows).
• Define basic telemetry and metrics to measure AI usage and impact (adoption, productivity proxies).
• Ensure solutions are secure, compliant, and cost-aware.
  
  

Requirements

Must-have

• Strong software engineering background (you don’t need to be a low-level ML expert).
• Hands-on experience working with LLM / GenAI tools in real projects.
• Practical experience with Python (for integrations, scripts, prototypes).
• Understanding of RAG patterns, embeddings, and vector-based retrieval at a conceptual and practical level.
• Experience working with cloud environments (AWS preferred, but not strictly required if fundamentals are strong).
• Ability to work independently end-to-end.
• Strong communication skills and confidence working with both technical and non-technical stakeholders.
• Very good command of English (working language).

Equally important (non-technical)

• Ability to listen, ask the right questions, and understand how people work.
• Pragmatic mindset — focused on adoption and usefulness, not perfection.
• Comfort with ambiguity and building standards from scratch.
• Willingness to say “this doesn’t make sense” when AI is not the right tool.

Nice to Have

• Experience introducing AI or tooling inside an organization, not just building standalone features.
• Experience with telemetry, dashboards, or basic data analysis.
• Familiarity with governance, security, or compliance topics related to AI.
• Background in consulting, enablement, or cross-team roles.
• Experience in scaling internal tools across multiple teams or countries.

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What We Offer

• Ownership of the AI domain in a large-scale project
• Real influence on architecture and strategy
• Freedom to choose modern AI technologies
• Long-term B2B cooperation
• 100% remote work and flexible hours
• Mature, collaborative engineering culture
  
  

Recruitment Process

1. Short call with a recruiter
2. Technical interview with RST team
3. Decision and offer