AI Engineer - EY GDS

Description

Summary: The Senior AI Engineer designs, builds, and ships enterprise-grade AI/ML and LLM-based solutions with a focus on hands-on engineering and strong collaboration. Highlights: 1. Design and deploy enterprise-grade AI/ML and LLM solutions 2. Collaborate with cross-functional teams on scalable AI systems 3. Ensure high engineering standards and best practices * **Location:** Buenos Aires \- Argentina (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