Manager AI Engineer - EY GDS

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
• Global stakeholder management
• Bachelor degree
• Preferred: Cross-cultural leadership