The AI manager own outcomes for the team’s LLM and GenAI portfolio by translating business problems into an LLM roadmap, leading delivery of production-grade LLM solutions, and ensuring reliability, scalability, governance, and measurable business impact.
Job Activities:
- Own the LLM product portfolio Define the GenAI roadmap, prioritize use cases, and deliver measurable business impact.
- Deliver production LLM solutions end to end Lead discovery → MVP → pilot → scale, with clear milestones, acceptance criteria, and success metrics.
- Set LLM technical standards and architecture Establish patterns for RAG, prompting, tool calling, evaluation, and monitoring so solutions are consistent and maintainable.
- Operate and govern LLM systems Ensure reliability, cost controls, security, privacy/PII handling, and responsible AI guardrails.
- Lead the team and execution cadence Coach engineers, run planning/reviews/demos, enforce accountability, and raise delivery quality.
- Manage stakeholders and adoption Align with business owners and partners (Product/Eng/Security/Ops), drive rollout, and communicate trade offs and progress.
- Own the LLM platform architecture - Define the reference architecture for LLM services (APIs, RAG layers, tool integrations, identity/access, secrets management) and ensure designs are scalable, secure, and reusable across teams.
- Establish DevOps/SRE practices for AI services - Implement CI/CD standards, environment promotion (dev/test/prod), release gating, automated regression/eval tests, rollback strategies, and on-call/incident workflows for LLM applications.
- Build observability and cost governance into the stack - Standardize logging/tracing/metrics, quality dashboards, token and latency monitoring, budget alerts, and usage analytics to control reliability and unit costs at scale.
Requirements
Language Level:
- Advanced English Level (C1)
Experience:
- 5+ years in software/AI roles with 2+ years focused on LLM/GenAI
Scholarship:
- Engineer Degree (Software Engineer would be a plus)
Specialized knowledge:
- Python and/or NodeJS for LLM application development
- Building and operating LLM applications in production (RAG, prompt/tool patterns, evaluation, monitoring)
- Service architecture for AI products (APIs, integrations, identity/access, secrets, environment separation)
- DevOps for AI services: CI/CD, release management, observability, incident response, and cost control
- API design, service integration, cloud deployment patterns
- RAG quality tuning (chunking, retrieval strategy, grounding, evaluation sets)
Benefits
- Christmas bonus (above law)
- Savings Fund & Voluntary Savings
- Profit Sharing (PTU)
- Vacation Days (above law)
- Vacation Premium
- Personal Days
- Major Medical Insurance
- Management Bonus