AI Engineers + Platform Architect - EY GDS
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
Apply To This Job
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
Apply To This Job