Summary: The AI Manager will own the LLM and GenAI portfolio, translating business problems into an LLM roadmap and leading the delivery of production-grade solutions. Highlights: 1. Own LLM and GenAI portfolio outcomes 2. Lead delivery of production-grade LLM solutions 3. Define GenAI roadmap and set technical standards **Job Description** 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. **Requirements** * Advanced English Level * 5\+ years in software/AI roles with 2\+ years focused on LLM/GenAI * 2\+ years leading teams and delivering cross\-functional products * 2\+ years leading teams and delivering cross\-functional products * Engineer Degree (Software Engineer would be a plus) * Python and/or NodeJS for LLM application development * API design, service integration, cloud deployment patterns * RAG quality tuning (chunking, retrieval strategy, grounding, evaluation sets) **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. **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