Postúlate en Kit Empleo: kitempleo.com.ar/empleo/q03x0

Job Description: AI & Data – AI Engineer

- 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

Postúlate en Kit Empleo: kitempleo.com.ar/empleo/q03x0

📌 AI Engineer - EY GDS (Buenos Aires)
🏢 WomenTech Network
📍 Buenos Aires