Postúlate en Kit Empleo: kitempleo.com.ar/empleo/q1zxf

Job Description: AI & Data – AI Manager

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

About the Role

The AI Manager leads technical strategy, oversees AI/ML engineering teams, and ensures high governance standards across enterprise AI programs. This role combines leadership, architecture, and cross-functional alignment.

Key Responsibilities

- Lead AI technical strategy, architectural decisions, design and roadmap execution of AI initiatives.
- Oversee engineering teams delivering AI/ML and LLM-based solutions at scale.
- Define and enforce technical standards, governance, and responsible AI practices.
- Partner with business and technical stakeholders to align AI initiatives with organizational goals.
- Provide coaching, mentorship, and development for AI engineers.

Skills & Qualifications

Python & Development

- Strong Python (+5 years)
- Technical leadership;
- Code reviews;
- Microservices architecture;
- Definition of technical standards
- Preferred: Performance optimization; legacy-to-AI-platform migrations; Distributed systems design
- We evaluate : Technical decisions; scalability; mentoring/coaching; standards

LLMs, RAG & Agents:

- Enterprise LLM design leadership;
- Governance, policies & risks;
- Strategy for RAG and agents;
- Continuous evaluation pipelines
- Preferred: Model/vendor selection (Azure/OpenAI/Anthropic/Mistral)
- What we evaluate : Strategy; risks; compliance; cost/safety criteria

Agent Orchestation





- Agent observability;
- Langchain
- Preferred: Langraph, autogen

Cloud (Azure or Databricks):

- Azure: Cloud architecture (security, networking, cost management, DRP); multi-cloud; AI landing zones.
- Databricks: Lakehouse governance & design; Lineage; granular permissions; Multi-workspace integration.
- Preferred: Cross-cloud residency/compliance, Cost strategy & optimization
- What we evaluate : Compliance; standards; scalability. Standardization; architectural decisions; cost control

MLOps & Delivery:

- Enterprise MLOps strategy;
- Model governance;
- AI SLAs (latency, grounding, costs);
- AI FinOps;
- Integration with client Data Governance
- Preferred: Hybrid MLOps (on‑prem + cloud)
- What we evaluate : Operation at scale; security; cost control

ML Fundamentals:

- Strategic model decisions for AI products
- Preferred: Model risk evaluation
- What we evaluate : Impact-driven judgment

AI Factory Design:

- Cloud/vendor selection;
- AI infrastructure evaluation (model catalogs, vector DBs, observability);
- Tooling choices (Databricks, Azure AI Studio, OpenAI, Anthropic);
- End-to-end governance
- Preferred: Adoption roadmap; reference playbooks; maturity metrics
- What we evaluate : Vision; ecosystem orchestration; risk & compliance

Communication and other requirements:

- C1 english executive communication
- Integral stakeholder management
- Bachelor degree
- Preferred: Cross-cultural leadership

Postúlate en Kit Empleo: kitempleo.com.ar/empleo/q1zxf

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