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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
General stakeholder management
Bachelor degree
Preferred: Cross-cultural leadership

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📌 Manager Ai Engineer - Ey Gds (Buenos Aires)
🏢 EY
📍 Buenos Aires