AI Platform & Applied AI Manager

Jobtailor; São Paulo; Presencial; BRL 450 000 - 750 000; Tempo integral

Resumo da oferta

Laqus is seeking a senior AI Platform Architect in São Paulo to co-create the company’s AI strategy and lead the architecture definition and implementation of a secure, scalable enterprise AI platform. You will enable technology, product, and business teams to experiment with, develop, and operate AI solutions, driving capabilities like LLM environments, model routing, and guardrails.

You will translate business problems into technical solutions, evaluate approaches with LLMs, ML, and

Descrição da oferta de emprego

• Co-create Laqus's AI strategy
• Lead the definition and implementation of the architecture for a secure, scalable, and cost-efficient enterprise AI platform
• Enable Technology, Product, and business teams to experiment with, develop, and operate AI solutions
• Define and evolve capabilities such as LLM experimentation environments and playgrounds, standardized catalogs, model routing, agent integrations with systems and guardrails, agent observability, data ingestion pipelines, and deployment of proprietary ML models
• Identify, prioritize, prototype, and implement AI use cases with the Business, Product, and Technology teams
• Translate business problems into technical solutions and evaluate approaches using LLMs, Machine Learning, proprietary models, deterministic rules, or combinations thereof
• Increase Laqus's organizational maturity and knowledge in Artificial Intelligence
• Create and lead workshops, technical forums, demonstrations, knowledge-sharing sessions, and internal articles
• Support teams in experimenting with and adopting the available tools
• Work directly with the Chief Technology Officer, with functional reporting to a committee composed of the Heads of Operations, Product, and Technology, with the CEO as the primary sponsor

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, Statistics, Mathematics, Physics, Data Science, or a related field
• Experience in technology, with a strong foundation in software engineering and Artificial Intelligence
• Practical experience architecting and building distributed Artificial Intelligence platforms for cross-functional use
• Experience in fast-growing, highly dynamic environments
• Practical knowledge of RAG techniques for LLMs, embeddings, semantic search, and vector databases
• Experience with context management, prompt engineering, and optimizing interactions with models
• Knowledge of frameworks and SDKs for developing AI applications, such as LangChain, LangGraph, and Semantic Kernel
• Experience building and deploying AI agents, including memory, tool/function calling, and human oversight
• Knowledge of evaluating LLM-based applications, including automated evals and human evaluations
• Knowledge of MCP and other model integration standards
• Experience orchestrating, monitoring, and evaluating agents in production
• Experience integrating and orchestrating multiple models and providers using an agnostic-first approach
• Knowledge of model routing, fallback, caching, and cost and latency optimization strategies based on workload
• Experience with open-weight and proprietary models
• Knowledge of monitoring AI-related consumption, performance, and costs
• Strong foundations in supervised and unsupervised Machine Learning
• Knowledge of classification, regression, clustering, and anomaly detection techniques
• Knowledge of neural networks and Deep Learning
• Experience with Machine Learning frameworks such as PyTorch and TensorFlow
• Knowledge of MLOps, including model training, validation, and deployment pipelines, observability, and drift monitoring
• Experience with model management tools such as MLflow and SageMaker
• Experience developing backend APIs and microservices
• Knowledge of developing and running applications in containers such as Docker
• Experience with Git and CI/CD practices
• Knowledge of security, permissions, authentication, and secrets management
• Advanced Python
• Knowledge of C# and the .NET ecosystem is a plus
• High autonomy, accountability, predictability, proactivity, stakeholder management, prioritization, assertiveness, structured thinking, excellent communication, and technical leadership

Core Competencies

Demonstrates expertise in architecting and implementing scalable AI platforms, with a strong foundation in Machine Learning and experience in developing AI applications using frameworks like PyTorch and TensorFlow. Capable of translating business needs into technical solutions while fostering collaboration across technology, product, and business teams.

Highest-signal resume keywords

• AI Platform Architecture
• Machine Learning Frameworks
• LLM Experimentation
• Model Management Tools
• Technical Leadership

ATS Optimization Keywords

Hard Skills

• Artificial Intelligence
• Machine Learning
• Python
• RAG Techniques
• Prompt Engineering
• MLOps
• Distributed Systems
• Neural Networks
• API Development
• Containerization

Soft Skills

• Stakeholder Management
• Structured Thinking
• Excellent Communication
• Proactivity
• Accountability

Industry Keywords

• AI Solutions
• Model Integration Standards
• Data Ingestion Pipelines
• Agent Observability
• Cost Optimization Strategies

Tools & Technologies

• PyTorch
• TensorFlow
• MLflow
• SageMaker
• Docker
• Git
• CI/CD
• LangChain
• LangGraph
• Semantic Kernel