AI Engineers + Platform Architect - EY GDS

AI Engineers + Platform Architect - EY GDS

Locación: CABA Otra ubicación: Solo ubicación principal Salario: Competitiva Fecha: 23 abr. 2026

Descripción del trabajo

ID de la Requisición: 1700446 Job Description: AI & Data – AI Engineer Location: LATAM (Remote / Hybrid) Clients: US‑based Enterprise Clients About the Role 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. Skills & Qualifications Python & Development Advanced Python (3–6 years); FastAPI; scikit-learn; API design; clean code; Preferred: intermediate SQL, Design patterns (clean architecture/hexagonal); microservices; advanced testing; Docker What we evaluate: 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 What we evaluate: Hallucination mitigation; grounding; cost/latency trade-offs; quality Cloud (Azure or Databricks): Cloud (Azure): Azure OpenAI; Azure AI Search; Azure ML; service integration; AKS/Container Apps; API Management Databricks: Advanced MLflow (registry/tracking/serving); Delta Lake; Unity Catalog; Feature Store; Vector Search Preferred: Workflows/DLT, What we evaluate: Secure & scalable architectures; integration; resilience, Pipelines; governance (Unity Catalog); productivity 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 What we evaluate: Reliability; diagnostics; automation ML Fundamentals: Classic models; Advanced metrics & trade-offs; When to use classic ML vs. LLMs Preferred: Advanced/ensemble models What we evaluate: Technical judgment; model validation Communication and other requirements: English: Fluent B2+ technical communication Autonomy in English, Technical clarity; Proactive Good at managing request gathering and handling Proactive communication