Senior AI Engineer

🕒 Abril 29

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AI Technology Partners

SiteLinkedInTodas as Vagas

11 - 50 funcionários

Fundada em 2020

🤖 Inteligência Artificial

🏢 Corporativo

☁️ SaaS

Artificial Intelligence • Enterprise • SaaS

AI Technology Partners é uma empresa de consultoria e soluções empresariais B2B que desenvolve, implementa e gerencia sistemas de IA generativa para organizações de médio a grande porte. A empresa oferece plataformas de chat de IA seguras para toda a empresa, soluções personalizadas para vendas, marketing, operações, RH, finanças e P&D, habilitação de desenvolvedores e TI (hospedagem de modelos privados, sistemas de dados vetorizados, governança), além de serviços de estratégia, treinamento e gerenciamento de mudanças. Suas ofertas enfatizam segurança, implantações hospedadas na nuvem ou de propriedade do cliente, componentes de código aberto e um modelo de entrega build-operate-transfer para acelerar a adoção de IA e a propriedade a longo prazo.

Descrição

• Design multi-agent architectures with robust state management, memory, and routing. • Choose and implement leading frameworks such as LangGraph/LangChain Agents, Microsoft AutoGen, CrewAI, LlamaIndex Agents, Semantic Kernel, or Haystack Agents—and justify trade-offs. • Build modular components (planners, tool registries, policy guards, evaluators) that are reusable across clients and domains. • Integrate enterprise tools and data sources via function/tool calling, webhooks, and event-driven flows (Queues/Service Bus/Functions). • Implement retrieval-augmented generation (RAG) patterns with vector stores (Azure AI Search, pgvector, MongoDB Atlas, Pinecone, Weaviate, Milvus) and structured knowledge (SQL/Graph). • Add deterministic fallbacks, circuit breakers, and caching to keep latency and cost predictable. • Define SLIs/SLOs for agent runs; implement tracing, metrics, and logging (e.g., Langfuse + OpenTelemetry) and build dashboards for run-level analytics. • Create evaluation harnesses (automatic + human-in-the-loop) using tools such as Ragas, DeepEval, promptfoo to measure groundedness, task success, safety, and cost. • Productionize with CI/CD, environment promotion, feature flags, and canary strategies; optimize cost-per-task and time-to-success. • Enforce content and safety policies (redaction, classification, guardrails) with policy-as-code; implement role/tenant isolation and data minimization. • Collaborate with security teams to align to ISO 27001/SOC 2/NIST/HIPAA/GDPR contexts; deliver audit-ready evidence for agentic workflows. • Build privacy-first patterns (no data exfiltration by default, least-privilege tool access, secure prompt/trace storage). • Work directly with enterprise client teams to translate business processes into agentic designs; present trade-offs and proofs-of-value that lead to production.

🎯 Requisitos

• 5–8+ years in software/platform engineering with recent production LLM applications (not just prototypes). • Hands-on expertise with agentic frameworks (one or more of: LangGraph/LangChain Agents, AutoGen, CrewAI, LlamaIndex Agents, Semantic Kernel, Haystack Agents) and tool/function-calling patterns. • Strong RAG engineering across vector DBs, chunking/embedding strategies, metadata/search ranking, and grounding techniques. • Proven track record building observable, cost-aware, and secure LLM systems (tracing, evals, guardrails, secrets/IAM, PII handling). • Solid software engineering fundamentals: Python/TypeScript, async patterns, APIs, testing, CI/CD, containerization. • Clear communicator who can interface with clients and write crisp technical docs. • Nice to have Azure-first experience (Azure OpenAI, Azure AI Studio, Azure Functions/Container Apps/AKS, Private Link/VNet, Key Vault, Entra ID). • Cross-cloud exposure (AWS/GCP) and hybrid integrations; experience with enterprise connectors (SharePoint/OneDrive, ServiceNow, Salesforce). • Experience with structured output, constrained decoding, JSON Schemas, and program-of-thought planning.

🏖️ Benefícios

• Competitive and flexible compensation package aligned to experience, scope, and engagement model. • Performance incentives • Equity participation • Learning & sharing culture with deep dives, brown bags, and support for certifications/publication. • Inclusive, flexible workplace.