Backend Software Engineer (Applied AI)
In All Media Inc
- Location
- Remote (Brazil)
- Employment
- Full-time
- Level
- Mid Level
About the Role
In All Media is a managed nearshore software provider connecting global talent with innovative client projects. This role focuses on building autonomous AI agents and integrating LLM capabilities into production enterprise systems using advanced backend architectures.
Skills
Perks
- Remote Work
Full job details
Location: Remote from LATAM
Contract Type: Full-time Contractor
Time Zone Alignment: CT
About In All Media
We are a Managed Nearshore Teams provider headquartered in Austin, specializing in building and embedding high-performing software development teams. From design to deployment, we deliver customized solutions by connecting global talent with innovative client projects. Our model allows you to work on international challenges, collaborate with diverse teams, and grow your career while being part of a company that values expertise, creativity, and impact.
Project Overview
In this role, you will be at the forefront of the AI revolution, supporting a high-impact project focused on integrating LLM capabilities into production enterprise systems. You will join a collaborative, fast-paced team dedicated to building the next generation of AI-powered software. The project involves architecting sophisticated multi-step task orchestration workflows, developing autonomous AI agents, and bridging AI models with external platforms using open integration standards like the Model Context Protocol (MCP). As a Backend Engineer, you will be instrumental in executing rapid prototyping and driving production support to deliver highly reliable, goal-oriented AI systems.
Key Responsibilities
AI Integration: Build AI-powered software and integrate Large Language Model (LLM) capabilities seamlessly into production enterprise systems.
Agentic Development: Design and develop autonomous AI agents, tool-calling systems, and multi-step task orchestration workflows.
MCP Integration: Leverage Model Context Protocol (MCP) or equivalent open integration standards (e.g., custom tool-calling interfaces, JSON-RPC adapters) to connect AI models with external platforms and tools.
Backend Architecture: Build and maintain scalable distributed software architectures, utilizing REST/gRPC APIs and applying strong system design principles.
Context & Reliability Management: Optimize context window management and implement strict reliability patterns for LLM execution at scale.
Agile Delivery: Execute across the full software lifecycle—from experimentation and rapid prototyping to production support—under tight delivery timelines.
Must-Have Skills
Engineering Experience: Hands-on software engineering, backend systems, or applied AI engineering experience. (4+ YEARS OF EXPERIENCE)
Core Programming Mastery: Advanced proficiency in Python and/or Java for backend development and algorithm execution.
Applied AI & LLM Systems: Direct, proven experience building AI-powered software or integrating LLMs into enterprise-grade applications.
Agentic Frameworks: Hands-on experience developing autonomous AI agents and multi-step workflows.
MCP Integration Literacy: Practical experience using Model Context Protocol (MCP), custom tool-calling, or JSON-RPC adapters.
Production Fundamentals: Strong grounding in distributed architectures, API design (REST/gRPC), and reliability patterns.
Mindset & Delivery: Proven ownership mindset, comfortable navigating both rapid experimentation and robust production support.
Fluent English: Excellent verbal and written communication skills for daily technical collaboration and documentation.
Nice-to-Have Skills
Advanced AI Architectures: Hands-on experience with Retrieval-Augmented Generation (RAG), multi-agent orchestration frameworks, semantic search, or vector database platforms (e.g., Pinecone, Qdrant, Milvus).
Enterprise Platform Integrations: Background integrating AI solutions with platforms such as Salesforce, Jira, Slack, or internal developer portals.
AI Observability & MLOps: Familiarity with cloud-native infrastructure (AWS/GCP/Azure), containerization (Docker/Kubernetes), and AI monitoring platforms (e.g., LangSmith, Arize, Datadog).
Safety & Lifecycle Management: Experience implementing model guardrails, safety protocols, evaluation suites, and prompt/version control management.
Time Zone & Collaboration
This role is 100% remote from LATAM and requires consistent overlap with teams in Central Time (CT). Flexibility is expected to accommodate key ceremonies, stand-ups, and collaborative sessions within standard Austin business hours.
Language
All interviews, documentation, and daily communication are in English.
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