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EY

3,7

AI Engineer - EY GDS

Buenos Aires

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

Ver evaluaciones sobre la empresa

Esta empresa ya no tendrá secretos para ti

Inicia sesión para buscar evaluaciones auténticas, calificaciones anónimas y datos sobre los sueldos antes de postularte.

3,7

Recomendar a un amigo
Aprobación del CEO
CEO: Fernando Paci
Calificaciones 2.636

EY

3,7

AI Engineer - EY GDS

Buenos Aires

EY

3,7

AI Engineer - EY GDS

Buenos Aires

Location: Buenos Aires - Argentina (Hybrid)
Clients: US‑based Enterprise Clients

About the Role

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

Ver evaluaciones sobre la empresa

Esta empresa ya no tendrá secretos para ti

Inicia sesión para buscar evaluaciones auténticas, calificaciones anónimas y datos sobre los sueldos antes de postularte.

3,7

Recomendar a un amigo
Aprobación del CEO
CEO: Fernando Paci
Calificaciones 2.636

6 empleos de Unity engineer en Argentina

Esta empresa ya no tendrá secretos para ti

Esta empresa ya no tendrá secretos para ti