Solutions Engineer

Cellular Sales Verizon Authorized Retailer • Knoxville, TN

1d • Onsite

About The Position

As a Solutions Engineer, you will serve as a bridge between business stakeholders and technology solutions, applying your expertise to evaluate organizational challenges and determine the most appropriate technical approach. In this role, that focus is specifically directed toward AI/ML technologies — assessing whether problems are best addressed by solutions such as image recognition, ML forecasting, LLM prompting, or agentic workflows, versus a more deterministic or data-driven approach. Cellular Sales is in the early stages of its AI adoption journey, and this role will work closely alongside an Enterprise Architect to establish patterns, evaluate emerging tooling, and develop repeatable solutions that can scale across the organization. The work is intentionally exploratory in nature, with a practical focus on delivering tangible business value at each stage. You will lead discovery conversations with departments, design and build proof-of-concept solutions leveraging cloud AI APIs, serverless architectures, and modern integration patterns, and guide the organization in responsible, effective adoption of AI tooling. You will work across functions to ensure that prototypes are secure, well-governed, and grounded in practical business value before any investment in full-scale development.

Requirements

• High School diploma or GED
• 5+ years of IT infrastructure or software engineering experience
• 3+ years of hands-on experience with cloud platforms (AWS, Azure, or Google)
• 1+ years of direct experience building solutions using AI/ML APIs or frameworks
• Demonstrated experience evaluating and prototyping solutions across multiple technology approaches

Nice To Haves

• Experience with cloud platforms, preferably AWS, and/or Azure
• Experience with serverless compute (AWS Lambda, Azure Functions) and API gateway patterns
• Experience with message queuing services (AWS SQS/SNS, Azure Service Bus, or similar)
• Experience with Python and/or TypeScript for scripting, API integration, and lightweight application development
• Experience with REST and/or GraphQL API design and consumption
• Experience developing lightweight frontend prototypes using React, Angular, or similar frameworks for demonstration and stakeholder review purposes
• Experience with CI/CD pipelines and version control (Git)
• Experience with containerization (Docker) and basic Kubernetes concepts
• Familiarity with cloud AI/ML services such as AWS Bedrock, AWS SageMaker, Azure OpenAI, or Azure ML
• Familiarity with building and orchestrating LLM-based workflows including agents, tool use, and retrieval-augmented generation (RAG)
• Familiarity with classical ML use cases including image recognition, forecasting, classification, and anomaly detection, and when each is appropriate
• Familiarity with prompt engineering techniques and LLM safety and guardrail patterns
• Familiarity with data pipeline patterns or ETL processes, particularly for preparing data for AI consumption
• AWS Certified: Solutions Architect Associate or Professional, Machine Learning Specialty, or Developer Associate
• Microsoft Certified: Azure Solutions Architect Expert, Azure Developer Associate, or Azure AI Engineer Associate
• Ability to assess total cost of ownership for AI prototypes, including API token costs, compute, and storage
• Strong documentation and technical writing skills

Responsibilities

• Partner with business departments to gather and analyze problem statements, translating operational challenges into clearly defined technical requirements.
• Evaluate whether a given problem is best addressed by an AI/ML solution (such as image recognition, ML forecasting, LLM prompting, LLM agents, or retrieval-augmented generation) or by a more appropriate deterministic or rule-based data solution, and clearly communicate that recommendation with supporting rationale.
• Design and build proof-of-concept prototypes using cloud AI services (AWS Bedrock, Azure OpenAI, Google Vertex, etc.), cloud-native components such as API gateways, message queues, serverless functions, and managed ML endpoints.
• Develop lightweight, functional web UIs to demonstrate prototype functionality to stakeholders, prioritizing clarity and usability over production-readiness.
• Implement appropriate authentication, authorization, and safety guardrails in all AI prototypes, including prompt injection defenses, output filtering, rate limiting, and role-based access controls.
• Evaluate and select among AI solution patterns — including zero-shot and few-shot prompting, fine-tuning, agentic workflows, multi-modal models, and classical ML pipelines — based on problem fit, cost, and feasibility.
• Architect event-driven and API-based integration patterns connecting AI services to existing enterprise systems and data sources.
• Assess AI capabilities embedded in commercial off-the-shelf (COTS) applications for security risks and operational effectiveness, including evaluating data handling practices, model transparency, vendor access controls, and potential for data exfiltration or unintended disclosure. Benchmark these capabilities against internally developed or self-hosted AI tooling to inform build-vs-buy decisions and flag compliance concerns.
• Document prototype architectures, evaluation findings, and recommendation reports in a format accessible to both technical and non-technical audiences.
• Stay current with the rapidly evolving AI/ML landscape, assessing new tools, models, and frameworks for organizational applicability.
• Collaborate with cloud infrastructure, security, and data engineering teams to ensure prototypes adhere to governance, compliance, and cost management standards.
• Mentor junior engineers and serve as an internal subject matter expert on AI solution design patterns and responsible AI practices.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: High school or GED