Principal Architect

This is a fully remote position, open to applicants in Brazil.

📋 Description

• Assist the Engineering Manager (EM) with daily responsibilities.

• Lead the execution of daily delivery tasks (planning, managing dependencies, unblocking issues, risk management) and ensure that commitments are fulfilled.

• Uphold engineering standards (definition of done, testing strategies, code quality, documentation, and operational readiness).

• Provide transparent engineering updates to stakeholders and escalate issues promptly when necessary.

• Facilitate team execution without direct management: assist with onboarding and workflows, mentor/coach engineers and tech leads, and share best practices.

• Ensure knowledge transfer and proper handover: document decisions, create runbooks, and outline key architectural choices; facilitate a seamless transition at the conclusion of the engagement.

• Collaborate with Product/Business to identify areas where agentic AI adds clear value (workflow automation, assisted decision-making, content acceleration).

• Convert requirements into technical objectives (latency, cost, quality, robustness, compliance) and define success criteria (KPIs, A/B testing, guardrails).

• Design agentic architectures: tool orchestration, planning, memory management, context management, retrieval (RAG), routing, multi-agent patterns, and human-in-the-loop processes.

• Implement production patterns: prompt/versioning, evaluation harnesses, regression tests, feature flags, canary releases, and monitoring for quality, cost, and latency.

• Engineer for resilience: incorporate fallbacks, timeouts, retries, circuit breakers, safe tool execution, sandboxing, and secrets management.

• Establish guardrails: create tool policies, implement content filtering, PII redaction, policy-as-code, access controls, auditability, and traceability (traces, conversations, decisions).

• Collaborate with IT/Security/Cloud teams to ensure privacy, security, and risk compliance at scale.

• Define and enforce quality gates prior to production (red-teaming, adversarial testing, bias, and hallucination risk management).

• Build and manage the ML value chain: data contracts, data quality, lineage, drift monitoring, dataset management, and training/inference pipelines.

• Oversee production operations for models and agentic services: deployment, scaling, observability, incident response, SLOs, and post-mortems.

• Industrialize continuous evaluation: conduct offline evaluations, create golden sets, establish human feedback loops, and develop scorecards.

• Coach tech leads and engineers on delivery practices, quality, and operational excellence (without direct people management).

• Enhance ways of working: focus on documentation, testing, product ownership (“you build it, you run it”), and incident readiness.

• Promote collaboration among software engineers, data engineers, data scientists, ML engineers, SRE, product managers, and security teams.

⛳️ Requirements

• Proven engineering leadership experience in delivering data/ML products into production (scalability, reliability, security).

• Strong understanding of modern LLM/agentic patterns: RAG, embeddings, tool-calling/function execution, memory management, evaluation, and tracing.

• Excellent foundational skills in software engineering (architecture, microservices, API design, testing, CI/CD) and cloud technologies (preferably Azure).

• Practical experience with MLOps/LLMOps: monitoring, deployment, governance, cost/latency optimization, and observability (metrics/logs/traces).

• Solid knowledge of data engineering (pipelines, orchestration, quality) and SRE practices (SLOs, incidents, runbooks).

• Servant leadership style with high standards and strong stakeholder alignment capabilities.

• Product-oriented mindset with a focus on impact (measurement, iteration, prioritization).

• Ability to clearly communicate complex topics (risks, trade-offs, technical roadmap).

• Comfort in navigating ambiguity and transforming emerging technologies (agentic/LLM) into actionable plans and standards.

• Nice to Have: Significant experience across software/data/ML, including notable technical leadership (people management experience is a plus but not mandatory).

• Proven experience in delivering ML/LLM solutions under real-world constraints (security, cost, performance, operations).

🏝️ Benefits

• Health and dental insurance

• Meal and food allowance

• Childcare assistance

• Extended paternity leave

• Partnerships with gyms and health and wellness professionals through Wellhub (Gympass) TotalPass;

• Profit Sharing and Results Participation (PLR);

• Life insurance

• Access to a continuous learning platform (CI&T University);

• Membership in a discount club

• Free online platform dedicated to physical, mental, and overall well-being

• Pregnancy and responsible parenting course

• Collaborations with online learning platforms

• Language learning platform

• And many more!