AI Developer Engineer

GlobalLogic; Buenos Aires; On-site; ARS 1,800,000 - 3,000,000; Full time

Job summary

GlobalLogic is seeking an experienced AI engineer in Buenos Aires to design and ship agentic AI systems. You will leverage Python as the primary language, build multi-agent pipelines with LangGraph and AgentCore, and integrate LLMs via AWS Bedrock and LangChain.

The role involves data source integration, tool use patterns, and cross-functional collaboration with CSMs and executives. You will deploy on AWS Bedrock, develop FastAPI services, and ensure secure, governed access to external data

Qualifications

• Proficient Python; production-grade code and data pipelines.
• Experience with multi-agent orchestration frameworks (routing, memory, tools).
• Experience integrating LLMs via APIs and prompt engineering.

Responsibilities

• Design and implement multi-agent pipelines using LangGraph and AgentCore.
• Build prompt pipelines and tool-augmented agents with LangChain; memory constructs.
• Deploy LLM-powered agents on AWS Bedrock with secure, scalable practices.
• Develop lightweight FastAPI services exposing agentic capabilities.
• Instrument and iterate solutions based on usage signals.
• Collaborate with cross-functional teams to translate business needs into architectures.

Skills

Python

LangGraph

LangChain

AgentCore

LLM integration

AWS Bedrock

API design

Data pipelines

Security & governance

Tools

LangGraph

LangChain

AgentCore

AWS Bedrock

Job description

The ideal candidate will have hands-on experience building and shipping agentic AI systems,

with deep proficiency in Python and the modern AI engineering stack:

• Python Expertise: Python is your primary language. You write clean, production-grade code, build robust data pipelines, and are comfortable owning the full lifecycle from
• Agentic Frameworks: Practical experience with LangGraph, LangChain, AgentCore, or comparable multi-agent orchestration frameworks — including agent routing, state management, tool use, memory, and evaluation.
• LLM Integration: Demonstrated experience integrating large language models via AWS Bedrock, Anthropic APIs, or similar — including prompt engineering, context management, and output validation.
• Data Source Integrations: Hands-on experience connecting applications to modern data platforms including Snowflake, Apache Iceberg, OpenSearch, or similar.
• MCP / Tool-Use Patterns: Familiarity with Model Context Protocol (MCP) or equivalent patterns for giving agents structured, governed access to external data systems.
• Cloud Platforms: Hands-on AWS experience, with Bedrock exposure strongly preferred. Familiarity with IAM, Lambda, and API Gateway a plus.

Beyond technical skills, we’re looking for someone who brings:

• Bias for Action: You ship, instrument, and iterate — you don’t wait for perfect requirements.
• Strong Communication: Ability to translate complex agentic system behavior to non-technical stakeholders clearly and concisely.
• Security and Governance Mindset: Awareness of responsible AI practices, data privacy considerations, and the importance of auditability in agentic systems.
• Collaborative Spirit: Comfortable working across functions and levels, from CSMs to the C-suite.

Job Responsibilities

Here’s how you’ll be making an impact:

• Build Multi-Agent Pipelines: Design and implement orchestrated multi-agent systems using LangGraph and AgentCore, including routing logic, evaluation loops, retry mechanisms, and agent specialization patterns.
• Develop with LangChain: Leverage LangChain to build sophisticated prompt pipelines, tool-augmented agents, memory constructs, and retrieval-augmented generation workflows.
• Deploy on AWS Bedrock: Leverage AWS Bedrock to host, invoke, and manage LLM-powered agents at scale, ensuring reliability, cost efficiency, and security compliance.
• Build FastAPI Services: Develop lightweight, production-ready API services that expose agentic capabilities to downstream consumers and orchestration platforms.
• instrument your work, and evolve solutions based on real usage signals.
• Collaborate Cross-Functionally: Partner closely with Customer Success, Sales, and Analytics stakeholders to translate business requirements into agentic architectures.