AI Software Engineer – United States (Remote) | Visa + Salary Insights

📍 Location: us

🏷 Type: Full-time

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Job Overview

A rapidly scaling AI technology organization partnered with HRCap is hiring an AI Software Engineer to build next-generation AI-native backend systems and autonomous agent infrastructures. This is a Mid-Level (3+ years) engineering role focused on designing scalable AI services powered by LLMs, vision-language models (VLMs), and multi-agent orchestration frameworks. The ideal candidate is comfortable operating at the intersection of distributed systems, machine learning infrastructure, and production-grade backend engineering. You will contribute to building global inference systems, deploying resilient AI APIs, and designing the runtime architecture for autonomous agents used in real-world enterprise environments.

📅 Job Timeline & Status

This role is in an actively expanding hiring phase with no fixed closing date, indicating continuous intake until strong candidates are secured. Given the high compensation band and frontier AI scope, competition is expected to be intense. Applicants should apply immediately as early submissions typically receive higher visibility during rolling technical evaluations.

🌍 Work Eligibility & Location

This is a Remote-first US engineering role, but candidates should expect strong alignment with US business hours and cross-functional global collaboration. International applicants may be considered depending on client-side compliance and visa flexibility, though this is not formally confirmed.

💰 Salary Intelligence

This compensation band places the role in a highly competitive tier for AI infrastructure engineering. The upper range suggests strong weighting toward candidates with production experience in LLM systems, distributed inference, and scalable backend architectures. Performance bonuses likely depend on system reliability, deployment success, and product impact metrics.

📊 Role Breakdown

This position centers on building production-grade AI systems where engineering precision directly impacts model performance and user-facing reliability. Approximately 40% of the role focuses on backend AI service engineering, including designing scalable APIs for LLMs, VLMs, and computer vision pipelines. Another 30% involves agent orchestration, where you will design runtime systems for autonomous agents with tool-calling, memory management, and structured reasoning workflows.

Around 20% of your time will be dedicated to deployment and infrastructure optimization, including distributed GPU inference systems, latency reduction, and cost-efficient scaling strategies across cloud environments. The remaining 10% involves cross-functional alignment with product, research, and business teams to translate applied AI requirements into production systems.

Key technical depth includes FastAPI/Django backend development, PostgreSQL optimization, and advanced AI system design patterns such as RAG pipelines, function calling frameworks, and MCP (Model Context Protocol) integration. Engineers are expected to operate with strong autonomy and deliver production-ready systems under real-world latency and reliability constraints.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a high-difficulty engineering role requiring deep system design and applied AI expertise. Candidates without production-level experience in scalable backend systems or AI orchestration frameworks will face significant competition from experienced infrastructure engineers and AI platform developers.

🚀 Career Impact

This role offers significant career acceleration in AI infrastructure engineering, positioning candidates for senior technical leadership in AI platform and autonomous systems development.

📋 Key Responsibilities

You will design and deploy AI-native backend systems capable of handling high-scale inference workloads. Responsibilities include building LLM-powered APIs, optimizing inference latency, and ensuring cost-efficient GPU utilization across distributed environments. You will also architect autonomous agent systems with tool-calling capabilities, structured memory layers, and multi-step reasoning pipelines.

Additional responsibilities include implementing RAG pipelines, integrating multi-modal AI models, and developing resilient backend infrastructure using modern frameworks such as FastAPI or Spring Boot. A critical part of the role involves ensuring system reliability through SLIs/SLOs, implementing circuit breakers, retries, and fallback mechanisms for production-grade AI services.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

This section is personalized by seniority.

🧠 Fit & Positioning Analysis

Evaluate your match before applying.

📅 Application Signals

This role shows strong hiring momentum with rolling evaluations and no defined cutoff, suggesting continuous candidate intake. Given the high compensation and advanced AI scope, expect high global competition and selective screening. Candidates with strong distributed systems and LLM engineering backgrounds may receive faster responses within the first review cycles.

Strong application urgency is recommended because similar AI infrastructure roles often close unexpectedly once pipelines are filled. Interview speed expectations are moderate-to-fast, typically progressing within a few weeks for shortlisted profiles.

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✅ Job Source & Verification

This listing originates from HRCap’s official AI recruitment platform, a globally recognized executive search and AI talent network. The information reflects a recently active posting (verified 2026-06-07) with consistent alignment to current AI infrastructure hiring trends. While exact internal deadlines are not publicly disclosed, the role remains actively open and continuously reviewed, indicating high hiring priority within the organization’s expanding AI engineering division.