Head of AI

Light-it; Buenos Aires; On-site; ARS 180,884,000 - 271,325,000; Full time

Job summary

Light-it is seeking a seasoned AI Engineering Lead to guide a team of AI engineers, shape the AI product roadmap, and drive production systems. You will stay hands-on in architecture, prototyping, and delivery, while setting standards, patterns, and best practices across the AI practice.

You will review architectures, hire and mentor engineers, translate client needs into technical solutions, and balance reliability, latency, and cost in healthcare-aware projects.

Qualifications

• Proven track record shipping production AI systems.
• Experience leading AI engineering teams.
• Excellent communication with clients and stakeholders.
• Ability to translate ambiguous problems into prototypes.
• Strong product and business mindset.

Responsibilities

• Lead, mentor, and develop a team of AI Engineers.
• Remain hands-on, contributing to architecture, prototyping, and production development.
• Define technical standards, patterns, processes, and best practices for AI development across Light-it.
• Review architectures and implementations and help the team make strong technical decisions.
• Identify gaps in the team and participate in hiring and developing AI engineering talent.
• Help define the roadmap and priorities of Light-it's AI practice.

Skills

Software engineering

LLM applications

Agentic AI

Context engineering

Communication

Team leadership

Client communication

English proficiency

Tools

LangGraph

LangChain

Langfuse

Tool integration

MCP

Vector databases

Knowledge graphs

Job description

• Strong software engineering background with experience building and operating production systems.
• Deep practical experience building applications with LLMs and agentic architectures.
• Strong experience with LangGraph, LangChain, Langfuse, or comparable frameworks.
• Experience designing and implementing LLM evaluations and evaluation pipelines.
• Strong understanding of agentic AI, workflows, tool calling, RAG, structured outputs, context engineering, and graph-based architectures.
• Experience taking AI systems from prototype to production.
• Strong understanding of observability, reliability, latency, and cost considerations in production AI.
• Experience working with multiple LLM providers and understanding their respective trade-offs.
• Ability to move quickly from an ambiguous problem to a working prototype.
• Strong product and business mindset. You care about solving the right problem, not just using the newest technology.
• Ability to communicate technical concepts clearly to clients, executives, product teams, and engineers.
• Experience leading and developing engineers while remaining technically hands-on.
• Strong written and spoken English.

We are looking for someone able to

• Lead, mentor, and develop a team of AI Engineers.
• Remain hands-on, contributing directly to architecture, prototyping, and production development.
• Define technical standards, patterns, processes, and best practices for AI development across Light-it.
• Review architectures and implementations and help the team make strong technical decisions.
• Identify gaps in the team and participate in hiring and developing AI engineering talent.
• Help define the roadmap and priorities of Light-it's AI practice.

Build Production AI Systems

• Architect and implement agentic AI systems, workflows, and LLM-powered applications.
• Design systems using frameworks and technologies such as LangGraph, LangChain, and Langfuse.
• Build reliable agent architectures involving tool calling, structured outputs, memory, state management, RAG, and multi-step workflows.
• Design robust evaluation systems and eval pipelines to measure quality, reliability, safety, and performance.
• Implement observability and tracing for production AI systems.
• Work on context engineering, prompting strategies, routines, and graph-based agent architectures.
• Make informed decisions about when to use agents, deterministic workflows, traditional software, or combinations of them.
• Optimize AI systems for reliability, latency, and cost.

Work With Clients and Stakeholders

• Participate directly in conversations with clients about AI strategy, opportunities, and implementation.
• Translate complex AI concepts into language that business and product stakeholders can understand.
• Work with stakeholders to identify problems and determine where AI can create meaningful value.
• Turn ambiguous requirements into technical approaches, prototypes, and production systems.
• Help scope AI initiatives and communicate technical trade-offs, risks, timelines, and opportunities.
• Demo AI capabilities and prototypes to clients and internal stakeholders.
• Act as a technical authority for AI during discovery and sales conversations when needed.

Build AI Responsibly in Healthcare

• Design AI systems with healthcare privacy, security, and reliability requirements in mind.
• Understand the implications of working with PHI and regulated healthcare environments.
• Design appropriate safeguards, human-in-the-loop mechanisms, fallbacks, and escalation paths.
• Consider hallucination risk, data privacy, model behavior, and observability when designing production AI systems.
• Work closely with Engineering and DevSecOps to ensure AI systems meet Light-it's security and compliance standards.
• Continuously evaluate emerging models, frameworks, tools, and approaches.
• Rapidly prototype new AI capabilities through Light-it's Innovation Lab.
• Experiment with new approaches to agents, workflows, context engineering, graph engineering, and human-AI collaboration.
• Evaluate models and providers including OpenAI, Anthropic, Google, AWS Bedrock, and open-source alternatives.
• Explore technologies such as MCP and new standards for connecting agents, tools, and systems.
• Separate technologies that create real value from hype.

Build an AI-Native Engineering Organization

• Help define how software engineering changes in an agentic world.
• Develop and improve Light-it's internal engineering harness and AI development workflows.
• Experiment with coding agents and tools such as Claude Code, Codex, GitHub Copilot, and emerging alternatives.
• Create reusable routines, agents, context, tools, and workflows that improve engineering productivity and quality.
• Help Engineering teams adopt AI effectively rather than limiting AI knowledge to the dedicated AI team.
• Measure the impact of AI-assisted engineering and continuously improve our internal practices.

Other valuable skills

• Experience building AI products in healthcare or another regulated industry.
• Experience with HIPAA and systems handling PHI.
• Experience with AWS and Amazon Bedrock.
• Experience with MCP and agent/tool infrastructure.
• Experience implementing AI-assisted software development workflows at an organizational level.
• Experience with vector databases, knowledge graphs, GraphRAG, or advanced retrieval architectures.
• Experience contributing to technical discovery, solution architecture, or pre-sales conversations.