Senior AI/ML Engineer – Remote (United States) | Visa + Salary Insights

📍 Location: us

🏷 Type: Not specified

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Job Overview

This Senior AI/ML Engineer role is a high-impact opportunity within an enterprise-grade voice AI organization building next-generation conversational AI systems for large-scale contact center automation. The company focuses on transforming customer service experiences using LLMs, NLP, and Generative AI to deliver human-like, scalable, and secure interactions. Ideal candidates are Senior-Level engineers (5+ years) who combine strong machine learning fundamentals with production engineering expertise and a product-driven mindset. This is a fully Remote (United States) position designed for engineers who thrive in fast-paced, innovation-driven environments.

🌍 Work Eligibility & Location

This role is designed for distributed teams, making it highly accessible to US-based professionals seeking a Remote AI engineering position. However, candidates requiring new visa sponsorship will not be considered, which narrows eligibility to domestic or already-authorized professionals. Strong asynchronous communication skills are critical due to cross-functional collaboration across engineering and product teams.

💰 Salary Intelligence

The compensation package is positioned strongly within the US AI engineering market, especially for Senior-Level professionals specializing in LLM systems and production ML infrastructure. The range reflects both technical depth and expected ownership over model development, deployment pipelines, and applied research translation.

📊 Role Breakdown

This role centers on advancing enterprise conversational AI by integrating cutting-edge research into scalable production systems. Engineers will work extensively with Python, PyTorch, and modern ML tooling while building systems powered by LLMs and retrieval-based architectures. A key focus is transforming experimental models into reliable, high-performance production services used in real-world contact center environments.

Approximately 40% of the role involves applied research and experimentation with generative models, while another 35% focuses on production engineering, optimization, and deployment pipelines. The remaining 25% is dedicated to cross-functional collaboration, architecture design, and aligning ML outputs with business performance metrics.

Candidates will also engage with vector search systems, potentially using tools like FAISS or Elasticsearch, to enhance semantic retrieval and conversational accuracy. Strong emphasis is placed on iterative development, where rapid experimentation leads directly to production improvements. The role requires balancing innovation with stability, ensuring AI systems remain scalable, interpretable, and efficient under enterprise workloads.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a highly competitive position targeting experienced AI engineers with deep production knowledge. Candidates must demonstrate both research fluency and engineering maturity, especially in deploying LLM-based systems at scale. Expect rigorous evaluation of system design thinking and applied ML problem-solving ability.

🚀 Career Impact

This role provides significant long-term value by placing engineers at the forefront of enterprise AI transformation. Exposure to production-grade LLM systems and conversational AI at scale accelerates progression into senior leadership or specialized AI architecture roles.

📋 Key Responsibilities

Engineers will be responsible for designing and deploying ML-powered conversational systems that enhance enterprise customer service platforms. Core duties include building and optimizing LLM-based pipelines, improving retrieval-augmented generation systems, and ensuring model outputs meet performance and reliability standards.

Additional responsibilities include collaborating with product managers to define AI-driven features, translating research breakthroughs into deployable systems, and maintaining scalable infrastructure using Python and PyTorch. Engineers will also contribute to system architecture decisions, optimize latency and cost performance, and support continuous model improvement through experimentation and monitoring in production environments.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

This section helps tailor your application for maximum impact depending on your experience level. Focus on demonstrating clear ownership of ML systems, measurable production outcomes, and familiarity with LLM deployment pipelines. Emphasize architectural decisions, latency improvements, and real-world AI system scaling experience.

🧠 Fit & Positioning Analysis

Evaluate your candidacy carefully before applying, as this role prioritizes production-level ML experience over theoretical knowledge.

📅 Application Signals

Hiring urgency is high due to demand for LLM engineers and enterprise AI adoption. Competition is strong, with many senior candidates targeting similar roles in voice AI and conversational systems. Fast application submission and strong technical positioning significantly improve selection probability.

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✅ Job Source & Verification

This listing is based on a verified recruitment post shared by Reval Recruiting on behalf of an enterprise AI software company specializing in voice automation. The information reflects the most recent available job details at the time of publication and aligns with standard Senior AI/ML Engineer market expectations in the United States.