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EY logo
EY

Building a #BetterWorkingWorld by providing trust through assurance and helping organizations grow, transform & operate.

AI Engineers + Platform Architect

Platform EngineerPlatform EngineerFull TimeRemoteMid LevelTeam 10,001+Since 1989H1B SponsorCompany SiteLinkedIn

Location

Latin America (LATAM)

Posted

40 days ago

Salary

0

Seniority

Mid Level

Job Description

AI Engineers + Platform Architect

EY

Role Description The Senior AI Engineer designs, builds, and ships enterprise-grade AI/ML and LLM-based solutions. This role focuses on hands-on engineering, high-quality delivery, and strong collaboration with cross-functional teams. Key Responsibilities - Design, build, and deploy AI/ML and LLM-based solutions in enterprise environments. - Collaborate with cross-functional teams (Data Engineering, Cloud, Product) to deliver scalable AI systems. - Ensure high engineering standards, maintainability, and best practices. - Participate in code reviews, architecture discussions, and solution design. - Support continuous improvement of AI delivery processes and tooling. Qualifications - Advanced Python (3–6 years) - FastAPI - scikit-learn - API design - Clean code - Preferred: intermediate SQL, Design patterns (clean architecture/hexagonal), microservices, advanced testing, Docker Requirements - Code quality; API design; troubleshooting; software architecture discipline; applied SQL - LLMs, RAG & Agents: - End-to-end RAG; LangChain/LangGraph - Vector search (FAISS or similar) - Fine-tuning (LoRA/QLoRA) - Advanced evaluation (RAGAS/TruLens/DeepEval) - Agent design - Autogen - Preferred: Llama Index; custom retrievers - Cloud (Azure or Databricks): - Azure OpenAI; Azure AI Search; Azure ML; service integration; AKS/Container Apps; API Management - Advanced MLflow (registry/tracking/serving); Delta Lake; Unity Catalog; Feature Store; Vector Search - Preferred: Workflows/DLT - MLOps & Delivery: - CI/CD (GitHub Actions/Azure DevOps) - Docker - AKS/Kubernetes - End-to-end ML pipelines - Basic monitoring (latency, cost, failures) - Preferred: AI observability (tracing/telemetry); advanced Bicep/Terraform - ML Fundamentals: - Classic models - Advanced metrics & trade-offs - When to use classic ML vs. LLMs - Preferred: Advanced/ensemble models - English: Fluent B2+ technical communication - Autonomy in English, Technical clarity - Proactive - Good at managing request gathering and handling - Proactive communication

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