HubMub
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EY

AI Engineers + Platform Architect - EY GDS

EY
Argentina · Full-time · Mid-Senior
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

Key Skills

Ranked by relevance

ai unity design patterns microservices python devops mlflow cloud sql
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Posted
May 06, 2026
Type
Full-time
Level
Mid-Senior
Location
Buenos Aires
Company
EY

Industries

Professional Services

Categories

Engineering Information Technology

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