• Design, build, and operate multi-agent workflows and tool-enabled agents • Architect and maintain end-to-end RAG systems • Evaluate and integrate LLMs and GenAI services • Develop and optimize prompting strategies • Define and own evaluation frameworks for generative outputs • Build scalable, secure AI infrastructure on AWS • Own the full deployment lifecycle for models and agents • Ensure data quality through rigorous validation and augmentation

• Develop ML models and supporting services for classification, clustering, similarity search, and prediction. • Implement RAG pipelines: document ingestion, embedding generation, vector indexing, and retrieval tuning. • Build APIs and microservices to expose model capabilities to enterprise systems. • Integrate ML components into existing DevSecOps pipelines (Azure DevOps, CI/CD workflows). • Implement duplicate detection, ticket routing, SLA prediction, and root-cause assist features. • Optimize model performance for latency, throughput, and accuracy. • Conduct model evaluation, error analysis, and iterative tuning. • Work with Data Engineer to align data pipelines with model input requirements. • Ensure outputs are explainable, auditable, and compliant with governance controls.

Role Description As an AI-First Developer on the R&D team, you will build AI-powered internal tools and data-driven systems that help teams across the organization make better decisions. You will work with behavioral and operational data from over 30 million websites that use our products and turn that data into analytics platforms, reporting automation, workflow intelligence, and AI-powered features that real teams use every day. This is not a WordPress development role. WordPress familiarity is a bonus for product context, but the core of this role is AI/ML engineering on top of an extraordinary data asset. You will sit between a Full Stack AI Developer and an AI ML Engineer - strong enough on the ML and data side to work intelligently with large-scale operational data, and product-minded enough to build tools that teams actually adopt. To love this role, here's the type of person you are: - You're a builder at heart, understanding how AI tools work under the hood. - You're a self-starter who can take a loosely defined problem and ship a working solution. - You prioritize the user and the decision over the pipeline and the model. - You're comfortable owning the full stack of an AI project. - You're an excellent communicator, fluent in both verbal and written English. - You have the curiosity and desire to learn and grow your skills. - You take pride in the quality and craftsmanship of your work. - You're a team player who is comfortable working alongside other developers. - You're excited about building internal tools and data systems that have a massive impact. Common responsibilities include (but are not limited to): - Building AI-powered internal tools and data-driven systems used across the organization. - Developing AI/ML-driven platforms that process and analyze large-scale operational data. - Designing and deploying RAG systems, LLM-powered features, and agentic workflows. - Building data pipelines that ingest, transform, and serve behavioral and operational data at scale. - Prototyping and shipping AI solutions that streamline internal processes. - Evaluating and selecting the right models, frameworks, and infrastructure for each use case. - Writing clean, well-tested, production-grade Python code. - Collaborating with product teams and stakeholders to understand their needs. - Keeping up with the rapidly evolving AI landscape. - Communicating with the team and supporting your peers. Qualifications - 3+ years of professional experience building AI/ML systems in production environments. - Strong proficiency in Python, including production-grade practices. - Hands-on experience with LLM frameworks and tools: LangChain, LlamaIndex, or similar. - Experience with vector databases (Pinecone, ChromaDB, FAISS, Qdrant, or similar). - Solid understanding of ML fundamentals. - Experience building and deploying APIs (FastAPI, Flask, or similar). - Familiarity with cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). - Experience with data processing at scale - SQL, pandas, Spark, or similar. - Competent with version control through Git and GitHub. - Exceptional troubleshooting skills. - Ability to keep complex systems simple. - Previous remote work experience. - Personal computer with reliable internet access. - Availability to participate in audio/video meetings and be available on Slack between the hours of 9 AM - 1 PM EST daily. Benefits - Competitive Salary. - Term Life Insurance and Accidental Death & Dismemberment for all full-time team members. - Health, Dental, and Vision Insurance benefits for full-time U.S. employees. - Health Insurance benefits for employees in various countries. - Work from your home. - Flexible PTO after 90 days of employment. - Holidays based on your location. - Paid Parental Leave. - Reimbursement for software, books, or courses that promote continued learning. - Coverage of all costs of company travel. - Additional perks including AM Welcome Box, Yearly Anniversary Gifts, and Technology Stipend. - Opportunity to solve challenging and meaningful problems. - Ability to work with some of the best people in the business. - No politics, no b.s., and no jerks. Location This is a remote position - our team is spread around the globe! Our home base is in Florida, USA, so company operating hours are 9am - 5pm ET (UTC -5). Inclusion Statement At Awesome Motive, we strive to have the broadest possible view of diversity. Awesome Motive is proud to be an equal opportunity workplace and is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or any other basis protected by federal, state, or local law. How to apply? If all of this sounds interesting, then please submit your application! Please clearly include the following in your cover letter: - A brief walkthrough of your AI/ML journey. - The most impactful AI system you've built in production. - Your first idea for using behavioral data from 30 million websites. - Profile links with code samples. - Links to any published research or contributions. - Other profile links if available. - Details about your experience, qualifications, personality, etc. We won't be able to individually respond to all applications, but if we feel you're a strong match, someone will be in touch shortly. Thanks and we look forward to hearing from you!

• Build and support AI-ready access to WAI catalog, customer, order, product, sales-channel, and related business data as directed by AI platform leadership. • Assist with Infor integration and other trusted data connections so AI outputs are grounded in approved business data sources. • Implement semantic, keyword, and structured search across catalog, OE number, fitment, cross-reference, technical specification, and product data. • Parse and structure PDFs, Excel files, fitment guides, manufacturer specifications, RMA forms, customer questions, chatbot conversations, and other technical or business documents. • Build and support retrieval-augmented generation (RAG), embeddings, vector search, document ingestion pipelines, prompt workflows, and AI assistant capabilities for internal users and distributor support. • Capture and organize real user questions, failed searches, corrections, successful answers, and feedback to support evaluation data, retrieval improvement, and future model tuning. • Assist with evaluating, deploying, and testing approved hosted or open-source models such as Llama, Mistral, Qwen, or similar models under technical direction. • Support AI workflows related to Amazon Business trends, listing performance, returns, customer demand, and other approved business intelligence use cases as assigned. • Document technical logic, data mappings, prompts, retrieval sources, test cases, known issues, and support procedures. • Test AI outputs for source grounding, accuracy, consistency, reliability, and alignment with acceptance criteria. • Monitor assigned workflows, troubleshoot defects, analyze failures, and recommend improvements to retrieval quality, data quality, model behavior, and workflow performance. • Collaborate with offshore and onsite team members, IT, data owners, and business-facing AI resources to deliver approved work within WAI standards. • Undertakes additional responsibilities and tasks as directed by management.