Senior professional responsible for designing, developing, and operating end-to-end Artificial Intelligence solutions — from data pipelines to deploying Machine Learning and LLM models in container-oriented environments (Kubernetes/OpenShift). Strong expertise in Python, MLOps, GenAI/RAG, and business domain understanding. Required Knowledge * Language and Backend * Proficiency in Python or JAVA for building APIs, services, and batch jobs (e.g., FastAPI/Flask, automation scripts, ETLs). * Good coding practices (logging, testing, package organization, virtualenv/poetry/pip, etc.). * Infrastructure, Containers, and Orchestration * Experience with containers: building, image optimization, multi-stage builds. * Experience with Kubernetes / OpenShift (production experience preferred): * Deploying and operating applications (Deployments, CronJobs, ConfigMaps, Secrets, Ingress/Routes). * Basic observability concepts: logs, metrics, and cluster troubleshooting. * MLOps / Data AI * Experience in classical Machine Learning (model and feature training, evaluation, versioning). * Experience in production-grade LLMs / GenAI (vLLM, KServe, OpenShift AI or similar). * Knowledge of data pipelines and integration with diverse data sources. * Experience with Kafka or other messaging/streaming systems for large-scale event ingestion. * Experience with Elasticsearch / OpenSearch or other search engines for data indexing, querying, and analysis. * Architecture and Integration * Ability to design reference architectures for AI solutions (batch, near real-time, synchronous APIs). * Integration of AI services with legacy systems, REST APIs, and databases. * Experience with Git, CI/CD, and best practices for version control and automated deployment. * Business Domain Knowledge * Ability to translate business requirements into AI technical solutions focused on value and governance. * Behavioral Profile * Senior/hands-on profile, with autonomy to propose architecture, implement, test, and deploy to production. * Strong communication skills with business, product, development, and operations teams. * Ability to produce clear documentation (READMEs, FAQs, data dictionaries, architecture flows, etc.). Desired Knowledge * Advanced GenAI and LLMs * Experience with RAG, vector stores, and embeddings. * Knowledge of LLM orchestration (agents, tools, supervised chain-of-thought, etc.). * Experience with models such as Llama, Falcon, or other self-hosted LLMs. * Tools and Ecosystem * Experience with OpenShift AI / KServe / vLLM in enterprise environments. * Awareness of model cost and performance monitoring (tokens, latency, throughput). * Data Engineering / Analytics * Knowledge of data modeling, ETL/ELT, and data quality best practices. * Experience building dashboards/reports to monitor AI usage, business metrics, and risk indicators. * Security and Governance * Awareness of data security, LGPD, anonymization, and access control in government/public sector projects. Best practices for model governance (auditing, traceability, query logging, basic explainability).