Descripción de la oferta
Overview
In this role you will design and implement scalable Generative AI architectures that bring production-ready AI capabilities to multiple industries. You collaborate with cross-functional teams to define end-to-end AI patterns, from data pipelines to model deployment and monitoring. You will lead LLM-based systems, apply prompt engineering, and ensure secure, observable, cost-efficient AI solutions. This is a hands-on, architecture-driven position with a focus on impact and practical delivery.
Compensaciones / BeneficiosWellbeing packBudget for trainingWelcome packFree Udemy accessBirthday day offCareer plan
ResponsabilidadesDesign scalable production AI architectures (Generative AI and ML)Define end-to-end AI solution patterns from data ingestion to deployment and monitoringArchitect and implement LLM-based systems including RAG pipelines, agents, and orchestration frameworksDefine model lifecycle strategies (training, deployment, monitoring, retraining)Design data and feature pipelines for AI/ML in cloud environmentsEnsure AI solutions are secure, scalable, observable, and cost-efficientCollaborate with Data, Software, and Cloud teams to integrate AI capabilities into platformsDefine standards for AI governance, evaluation, and responsible AI usageEvaluate new AI technologies for business use casesSupport prompt engineering strategies, evaluation frameworks, and AI experimentationAct as technical reference for AI architecture decisions across projects
Requisitos principalesHands-on experience with generative models and AI agents in productionProficiency with Transformers, CNNs, GANsExpertise in prompt engineering (Chain-of-Thought, ReAct, Tree-of-Thought)Mastery of TensorFlow, PyTorch or similar frameworksNLP experience with embeddings, vector search, fine-tuningExperience orchestrating LLMs and conversational agents (LangChain, LangGraph, DSPy, CrewAI, Google ADK)LLM monitoring and evaluation using LangSmith, LangFuse or similarData management at scale (SQL, NoSQL, FAISS, Pinecone, Weaviate, ChromaDB)Model optimization and deployment techniques (quantization, distillation, vLLM, Triton, ONNX Runtime)Cloud and DevOps for AI (Azure, AWS, GCP, SageMaker, Vertex AI, Azure AI Foundry, Kubeflow, MLflow, Metaflow, BentoML)API development for LLMs and pipelines (FastAPI, Flask, gRPC)Advanced Python programmingProactivityTeamwork in diverse environmentsAnalytical thinkingPython (Advanced)Generative AI Expertise (GPT, Claude, Mistral, Llama)Core AI Frameworks (TensorFlow, PyTorch)
