AI Solutions Lead

Technical leader of the AI solutions practice: accountable for the architecture and quality of solutions the team takes from pilot to production, and for the technical management of the team.

Scope of responsibility

• Technical architecture of AI solutions: choosing the approach (RAG, fine-tuning, agentic pipelines, classical ML) and assessing the cost / quality / time-to-deploy trade-offs
• Decomposing business requirements into a technical backlog; effort estimation and building estimates with appropriate buffers
• Technical management of a team of 5-10 people: task allocation, code review, mentoring, competency development
• Ownership of delivering projects to a measurable business outcome rather than to a demo
• Establishing the quality loop for models: metrics, evaluation sets, regression testing of prompts and pipelines

• Technical communication with clients: defending architectural decisions, participating in presales and proposal development

   

Must-have requirements

• 5+ years in  ML, including 2+ years in a technical leadership role (team lead, tech lead)
• Hands-on experience taking LLM solutions to production: RAG, vector stores, agent orchestration, working within context and model constraints
• Production-level Python; solid understanding of API integrations, queues, and asynchronous pipelines
• Experience with cloud platforms (Azure / AWS / GCP), containerisation, and basic CI/CD
• Estimation and planning skills: able to produce a realistic estimate and explain what it consists of
• Ability to hold a technical conversation with business stakeholders without falling into jargon

• English at Upper-Intermediate level or above (documentation and client-facing work)

   

Nice to have

• Experience in a consulting or project-based delivery model (multiple parallel clients, fixed scopes)
• Experience building LLM output quality assessment processes (LLM-as-judge, human-in-the-loop)
• Experience with as-is / to-be process mapping and identifying automation opportunities
• Experience introducing AI-assisted development practices (agentic coding tools, conventions) to a team

• Experience with self-hosted model deployment and its cost economics

   

Soft skills and management profile

• Takes ownership of the team's results, not only of their own code
• Able to tell a client "this won't work" and propose an alternative
• Develops people: walks through the reasoning behind decisions rather than simply correcting them
• Comfortable working with unstable requirements — the normal condition of AI projects
• Willing to run difficult performance conversations, not only supportive mentoring