About the role

AI/ML Engineer developing, optimizing, and scaling machine learning models for US Mobile’s next-gen user experiences. Collaborating with teams to enhance connectivity through innovative solutions.

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.
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.
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.
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.
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.

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.
Experience with vector search solutions (e.g. Pinecone, Weaviate, or Elasticsearch with vector plugins).
Experienced in building or deploying large language models and related tooling in the AWS Bedrock ecosystem.
Familiarity with to multi-agent LLM frameworks or Orchestrations (e.g., specialized agent-based approaches in advanced NLP.
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.
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.
Comfortable presenting complex ML concepts to non-technical stakeholders
Passion for iterative development—able to pivot based on user feedback and product metrics.