AI Solutions Engineer

This role begins as a contract position at an hourly rate of $200 USD per hour, providing a streamlined path toward a permanent, salaried full-time transition. Please note that while the contract phase offers a higher hourly rate in lieu of benefits, the full-time conversion introduces a comprehensive total rewards package, including premium health coverage and retirement programs, alongside a restructured annual salary. Next League is seeking an AI Solutions Engineer to design, build, and ship production AI systems across our portfolio of sports, entertainment, and league clients. This is the engineering counterpart to our Data Scientist and AI Product Manager roles, and the person who turns prototypes and discovery work into deployed, scaled, and integrated solutions. The role combines two core capabilities: building AI agents and applied LLM systems, and engineering computer vision pipelines that power fan-facing experiences and content operations. You will work across the full lifecycle, from architecting a solution with a client, to shipping a v1, to integrating it into the client's stack and hardening it for production. You will partner closely with AI Product Managers, data scientists, and client engineering teams. Clients include professional teams, leagues, major sports properties, and national governing bodies. Projects may span: AI agents, copilots, and assistants for sales, service, content, and internal workflows Retrieval-augmented generation (RAG) and tool-using agent systems Computer vision pipelines for fan-facing experiences (mobile, AR, in-venue activations, second-screen experiences) Computer vision for content operations (highlight detection, automated tagging, content moderation, metadata enrichment) Multi-modal AI systems that combine LLMs and vision models Production deployment, MLOps, and integration into client CRM, ticketing, content, and marketing platforms Evaluation, monitoring, and guardrail systems for agent and CV solutions Essential Duties and Responsibilities The following and other duties may be assigned as necessary: AI Agent & Applied LLM Engineering Build and ship AI agents, copilots, and RAG systems using leading frameworks (LangChain, LangGraph, OpenAI Agents SDK, Claude, and similar) Design tool-use patterns, function calling, and orchestration logic for multi-step agent workflows Develop evaluation rubrics and harnesses for agent quality, covering accuracy, latency, cost, and safety Optimize prompts, retrieval pipelines, and model selection for production performance Computer Vision Engineering Engineer CV pipelines that power fan-facing experiences across mobile, AR, in-venue, and digital channels Build content operations tooling including automated highlight detection, tagging, moderation, and metadata enrichment for video and image assets Train, fine-tune, and evaluate vision models (object detection, segmentation, classification, embedding, multi-modal) Integrate vision systems with downstream content management, marketing, and fan engagement platforms Production Systems, Deployment & Integration Deploy AI and CV systems into production environments with appropriate monitoring, logging, and observability Integrate solutions into client systems including CRM (Salesforce, HubSpot), ticketing, content management, and marketing automation platforms Optimize systems for cost, latency, throughput, and reliability Contribute to responsible AI, governance, privacy, and enterprise data handling practices Client Engagement & Delivery Partner with AI Product Managers and consultants to translate discovery into shippable technical solutions Operate as a hands-on technical lead on client engagements, with occasional embedding for solution design, integration, and hardening Communicate technical tradeoffs to non-technical stakeholders with clarity and credibility Contribute reusable patterns, templates, and reference implementations that scale Next League's delivery across accounts Qualification Requirements To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. 3 to 5 years of experience as an ML engineer, AI engineer, or software engineer shipping production AI/ML systems Strong Python proficiency, with comfort in at least one of TypeScript, Node.js, or Go for production services Hands-on experience building and deploying AI agents, copilots, or RAG systems using modern agentic frameworks Hands-on experience building production computer vision systems, including model training, fine-tuning, or evaluation Demonstrated ability to take AI/ML work from prototype to production with appropriate monitoring and reliability practices Experience working with large language model APIs and at least one major model provider (Claude, OpenAI, Gemini, or similar) Strong communication skills and the ability to operate in client-facing technical conversations Comfort with system design fundamentals: APIs, authentication, retrieval, event tracking, queues, and storage Preferred Experience Sports, entertainment, league, media, or content industry experience strongly preferred Hands-on experience with leading AI platforms such as: Claude ChatGPT / OpenAI APIs Google Gemini / Vertex AI Microsoft Copilot (M365 / Copilot Studio) AWS Bedrock Azure AI Foundry Experience with agentic frameworks (LangChain, LangGraph, OpenAI Agents SDK, CrewAI) Experience with video processing pipelines, automated highlight generation, or content moderation systems Experience with AR or mobile computer vision (ARKit, ARCore, WebAR, on-device inference) Familiarity with vector databases (Pinecone, Weaviate, pgvector) and embedding workflows Experience with cloud platforms (AWS, GCP, Azure) and MLOps tooling Familiarity with marketing, CRM, ticketing, content management, or fan engagement platforms Experience with automation tools such as n8n or Zapier Background in responsible AI practices, model governance, and enterprise data handling Who Will Thrive in This Role Engineers who love shipping, and who measure their work in production systems and business outcomes Builders who can move fluently between agent orchestration and computer vision pipelines Pragmatists who can architect cleanly, ship fast v1s, and harden them into production Practitioners excited to apply AI across fan experiences, content operations, and live sports environments #LI-DNI

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