Your missionOwn and evolve our AI engineering function — transforming a 15–20 person ML team from research-heavy to a high-throughput, production-grade organization.
You’ll partner with the CTO on strategy, build the platform that unifies LLM access, RAG, and backend services, and ship reliable, scalable AI features that change how banks work.
Responsabilidades
Team leadership and org buildHire, mentor, and develop a high-performing team; set the technical bar, operating rhythms, and code/research review practicesOrganize sub-teams (e.g., Core Modeling, AI Platform/Infra, Integrations) with clear ownership, SLOs, andon-callManage roadmap, capacity planning, and delivery across parallel initiativesArchitecture and platformOwn the LLM gateway: unified APIs and proxy layers for multi-provider routing (OpenAI, Gemini, Bedrock), with rate limits, fallbacks, and cost trackingBuild high-performance RAG pipelines (ingestion, embeddings, vector stores, caching) with robust observability and safety guardrailsPartner with Java/NestJSteams to define clean async contracts, schemas, and eventing patterns; drive low-latency, scalable inferenceModel lifecycle and operationsLead end-to-end model and prompt lifecycle: data curation, training/fine-tuning, evaluation, deployment, rollbackEstablish LLMOps/MLOps: model/prompt registries, CI/CD, canary/A/B tests, offline/online evals, drift and cost monitoringOptimizeinference throughput and cost (autoscaling, batching, quantization/distillation, caching)Strategy and collaborationTranslate company goals into an AI/ML roadmap with measurable outcomes; balance exploration with reliability and costOwn build-vs-buy/vendor strategy for models, infrastructure, and data services; manage budgets and SLAsGovernance and securityImplement data privacy, security, and compliance practices (RBAC, secrets, auditability); track prompt/model lineage and reproducibilityDefine incident response, runbooks, and postmortems for AI featuresYour profile5+ ...