Offer summary

Qualifications:

3+ years of hands-on experience in building and deploying machine learning solutions at scale., Solid understanding of NLP techniques, including transformer models and embeddings., Proficient in Python or similar languages for data pipelines and model development, with experience in cloud platforms like AWS., Skilled in diagnosing bottlenecks and scaling solutions, with a passion for iterative development..

Key responsabilities:

Design and deploy conversational and multi-agent LLM solutions to handle user requests.

Work with cross-functional teams to design data pipelines for real-time model data.

Ensure the chat system can handle thousands of concurrent interactions while maintaining performance.

Stay current on AI/ML trends and propose innovative ways to extend AI across the platform.

Job description

US Mobile is on a mission to revolutionize connectivity. Imagine a world where you can go into a single app and buy terabytes of data for every one of your devices: phone, smart devices, car, home broadband, and more. That’s the future that US Mobile is building: a software platform built truly for the 21st century and the age of 5G and IoT, with world class engineering, best-in-class user experience, and features that will define the next generation of connectivity.

At the core of it all, we have a team and culture that has been recognized by Forbes as one of the top 500 best startup employers in the US. Our team spans diverse backgrounds, cultures, and stories, with employees coming from 20+ countries.

We're a venture-backed company entering hypergrowth, having recently ranked 94th on Inc 5000's fastest-growing private companies in America, and we’re looking for someone exceptional to join our team.

Job Description:

We’re looking for an AI/ML Engineer who will develop, optimize, and scale machine learning models that power our next generation of user experiences. Working closely with product, engineering, and design, you’ll ensure our ML tools truly address user needs—whether they’re discovering new features, troubleshooting connectivity, or receiving proactive solutions to common issues.

Key Responsibilities:

Design & Deploy Conversational / Multi-Agent LLM Solutions

Craft multi-agent conversational flows capable of handling a wide range of user requests—both purely informational and action-oriented.

Employ advanced LLM techniques (prompt engineering, context retrieval, multi-step reasoning) to ensure robust, context-aware dialogues

Multi-Modal & Multi-Model Integration

Explore different input/output formats (e.g., text, potential voice or image-based flows) to enrich user interactions

Evaluate different models based on their intended use case, considering both technical capabilities and cost efficiency

Platform & Pipeline Building

Work with cross-functional teams to design data pipelines that feed your models real-time or near real-time data

Implement best practices around model lifecycle management—versioning, containerization, deployment orchestration, etc

Optimization & Scale

Ensure the chat system can handle thousands (eventually millions) of concurrent interactions, maintaining low latency and high availability

Monitor performance, define metrics (latency, user success rate, fallback rate, etc.), and iteratively improve

Ongoing Innovation & Experimentation

Remain current on the rapidly evolving AI/ML landscape, especially in generative models, multi-agent orchestration, and knowledge retrieval

Propose new ways to extend AI across our platform—e.g., advanced personalization, proactive customer engagements, etc.

Qualifications:

Core AI/ML Expertise

3+ years hands-on experience building and deploying machine learning solutions at scale

Solid understanding of NLP techniques, including transformer models and embeddings, with hands-on experience using modern tools like Hugging Face, AWS Bedrock, and OpenAI’s API

Backend & Data Infrastructure

Proficient in Python or a similar language for data pipelines and model development

Experience with cloud platforms (AWS strongly preferred), containerization (Docker, Kubernetes), and microservices

Research & Problem-Solving Mindset

Up-to-date on AI/ML trends—especially in multi-agent systems, generative modeling, or multi-modal approaches

Skilled at diagnosing bottlenecks, scaling solutions, and balancing innovation against real-world constraints

Collaboration & Communication

Comfortable presenting complex ML concepts to non-technical stakeholders

Passion for iterative development—able to pivot based on user feedback and product metrics.

Bonus Points:

Familiarity with vector search solutions (e.g. Pinecone, Weaviate, or Elasticsearch with vector plugins)

Familiarity with building or deploying large language models and related tooling in the AWS Bedrock ecosystem