Backend Software Engineer (Applied AI)

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.

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.

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.

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.

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.

All interviews, documentation, and daily communication are in English.

#LI