Job Title: AI/ML Engineer Location: Dayton, OH and Remote Employment Type: Full-Time Clearance requirements: TS/SCI About the Role Rackner is seeking a highly skilled AI/ML Engineer to design, develop, and deploy advanced machine learning solutions that support mission-critical systems. This role will focus on building scalable models, developing training pipelines, and collaborating with cross-functional teams to deliver impactful AI-driven solutions. Key Responsibilities - Design, develop, and implement machine learning and deep learning models - Build and optimize model architectures including CNNs, RNNs, and transformer-based models - Develop and deploy Large Language Models (LLMs) and object detection systems (e.g., YOLO, Faster R-CNN) - Perform feature engineering and prepare high-quality datasets for training and evaluation - Create and maintain AI/ML training runbooks and documentation - Collaborate with data engineers and software teams to integrate models into production systems - Ensure reproducibility through data versioning and metadata standards - Continuously evaluate and improve model performance and scalability Required Qualifications - Strong proficiency in designing and implementing model architectures, including: - Convolutional Neural Networks (CNNs) - Recurrent Neural Networks (RNNs) - Transformer-based architectures - Large Language Models (LLMs) - Object Detection models (e.g., YOLO, Faster R-CNN) - Hands-on experience with: - PyTorch and/or TensorFlow - Hugging Face, Ollama, or similar frameworks - Experience with data engineering concepts, including: - Feature engineering and dataset preparation - Data versioning tools (e.g., lakeFS) - Metadata standards such as STAC - Ability to create clear and effective AI/ML training runbooks - Strong problem-solving skills and ability to work in a collaborative environment Preferred Qualifications - Experience deploying models in cloud-native environments - Familiarity with DevSecOps practices - Experience working with large-scale or federal datasets - Understanding of MLOps principles and pipelines Benefits & Perks - Weekly pay with full remote flexibility - Professional growth investment, including paid certifications and training - Comprehensive benefits package, including: - Medical, dental, and vision coverage - 401(k) with 100% company match up to 6% - Paid time off (PTO) - Life and disability insurance - Home office equipment plan - A supportive, inclusive team culture focused on collaboration, trust, and mission impact About Rackner Rackner is a cloud-native software consultancy delivering solutions for startups, enterprises, and the public sector. We enable digital transformation through DevSecOps, AI/ML, and cloud-first innovation. Our teams solve high-impact problems that advance federal missions and strengthen national readiness. Join us to help shape the future of secure, scalable data systems supporting mission success.

Role Description The Senior Principal Product Enablement Specialist, AI will serve as a senior technical enablement expert responsible for enabling AI-powered platform capabilities across the Simulations Plus ecosystem. This role focuses on the enablement, implementation, and operational readiness of AI agents, AI-assisted workflows, and the composition layer that supports cross-product functionality. This individual contributor role provides the opportunity to shape how AI capabilities are delivered and adopted across products while remaining hands-on with implementation, testing, and customer enablement. Job Responsibilities - AI Product Enablement Leadership - Define and establish enablement best practices for AI-powered capabilities across the ecosystem - AI composition layer integration - Shared AI agents (e.g., data agents, reporting agents) - Cross-product AI workflows - Develop implementation frameworks for deploying AI capabilities consistently across products - Establish standards for AI feature testing, validation, and operational readiness - Create reusable implementation patterns and configuration templates for AI capabilities - Drive standardization of AI documentation, enablement materials, and training resources - AI Capability Implementation - Implement AI agents and AI-assisted features within product workflows - Integrate AI agents with product APIs and services - Configure AI pipelines and execution environments - Adapt shared AI capabilities to product-specific contexts - Collaborate with engineering and product teams to operationalize AI features - Support integration of shared AI services into multiple product environments - Identify and resolve implementation challenges during development and deployment - Ensure AI features function consistently across the product ecosystem - AI Feature Testing and Validation - Develop and execute testing frameworks for AI-powered product capabilities - Functional testing of AI agents - Evaluation pipelines for AI outputs - Reliability and performance testing - Implement human-in-the-loop validation strategies for AI-assisted workflows - Identify edge cases and failure modes in AI-driven features - Work with engineering teams to improve reliability and monitoring of AI systems - Establish quality assurance practices specific to AI-enabled product features - Documentation and Training - Develop comprehensive documentation for AI-enabled product capabilities - Architecture and usage documentation - Implementation and integration guides - Troubleshooting resources - Create training curricula for internal teams and customers adopting AI capabilities - Produce educational materials explaining AI workflows and agent capabilities - Ensure documentation remains aligned with evolving AI platform capabilities - Support onboarding and training of teams implementing AI-powered workflows - Operational Support and Troubleshooting - Serve as a technical escalation point for complex AI feature deployments - Diagnose and resolve issues related to AI agents, pipelines, and workflows - Support services and support teams in troubleshooting AI-enabled functionality - Identify patterns in operational issues to improve AI reliability and usability - Contribute improvements to operational practices for AI-powered features - Cross-Product Collaboration - Collaborate with Product and Engineering teams to ensure consistent AI capability delivery - Work with Services and Support teams to capture operational insights from AI deployments - Provide feedback to product teams on usability and adoption challenges - Identify opportunities to reuse AI capabilities across products and workflows - Help maintain alignment between AI platform capabilities and product-level implementations Qualifications - 7+ years of experience working with AI-enabled software products or AI platforms - Experience implementing LLM-based systems, AI agents, or AI pipelines - Experience with evaluation frameworks for AI outputs - Strong understanding of AI reliability, monitoring, and operational practices - Ability to translate AI capabilities into practical product functionality - Experience implementing complex software systems or platform capabilities - Working with APIs and distributed services - Strong programming or scripting experience (Python or similar languages commonly used in AI development) - Experience working with modern AI tooling and development frameworks - Demonstrated ability to support multiple product environments simultaneously - Strong technical communication and documentation skills - Ability to collaborate effectively with product, engineering, and operations teams - Strong organizational skills and ability to manage multiple initiatives Preferred Qualifications - Experience working with AI agent architectures and orchestration frameworks - Experience implementing retrieval-augmented generation or knowledge-based AI systems - Familiarity with AI evaluation pipelines and observability tooling - Experience deploying AI capabilities in enterprise software environments - Familiarity with cloud-based AI infrastructure and deployment models - Experience supporting cross-product platforms or shared services - Understanding of API-based product architectures Education - Bachelor’s degree in computer science, data science, artificial intelligence, or related field - Masters or PhD in computer science, data science, artificial intelligence, or related field preferred

• Build AI-Powered Features: Design, develop, and deploy production-grade AI applications that solve real customer problems, from concept to deployment • Architect Scalable Systems: Create robust backend architectures that support AI workloads, ensuring low latency, high reliability, and seamless integration with existing services • Drive AI Innovation: Implement and optimize agentic AI systems, RAG pipelines, and multi-agent workflows using modern LLM frameworks • Own the Full Stack: Take end-to-end ownership of features, from database design and API development to AI model integration and monitoring • Collaborate Cross-Functionally: Work closely with product, design, and data teams to translate business requirements into technical solutions • Optimize and Iterate: Run experiments, A/B tests, and evaluations to continuously improve AI system performance and user experience

Saga is building the infrastructure and products for the next generation of AI agents. Our AI Character Agent Network allows studios, creators, and publishers to turn their most iconic IP into living, breathing digital companions that engage fans across social media, drive organic user acquisition, and power a new kind of agentic commerce. Our agents run on a proprietary training architecture that combines LLMs and SLMs to produce fine-tuned character AI agents — autonomous enough to develop their own intelligence and personality, while guided by the creative vision of the original studios and creators. We've processed over 23 million transactions worth more than $2 billion since our early 2024 launch, and we're just getting started. Our team is world-class — veteran web3 entrepreneurs and builders from Apple, X (formerly Twitter), Robinhood, Samsung, NVIDIA, Tendermint and Skuchain — and we're looking to grow as we scale our agent platform to more studios, more platforms, and more users. We offer a flexible, remote working environment. You can expect a warm welcome from a friendly and international team that will support you in your personal and professional growth. The Role As a Senior AI Agent Engineer, you'll be at the center of how we build, train, deploy, and operate character AI agents at scale. You'll work across the full lifecycle — from fine-tuning models and orchestrating SLM swarms to deploying agents across social platforms and building the infrastructure that keeps them running reliably. This role blends ML engineering, backend development, and systems thinking. The Work - Build and maintain the training and inference pipelines for character AI agents, including LLM/SLM orchestration and swarm-based architectures - Deploy and operate AI agents across social media platforms (Instagram, X, WhatsApp, TikTok) with consistent character behavior and personality coherence - Develop tooling for studios and creators to customize agent personality, lore, brand guidelines, and behavioral guardrails - Build and improve the feedback loops — fine-tuning, reward models, RLHF, and RLAIF — that keep agents improving over time - Architect and scale the infrastructure supporting agent deployments, including multi-modal capabilities (text, voice, video, livestreaming) - Contribute to the agentic commerce platform — enabling agents to drive transactions, recommend products, and interact with payment systems - Implement safety and content moderation systems to ensure agents behave appropriately across diverse user interactions - Collaborate with studio partners to translate creative direction into agent training parameters and behavioral specifications - Monitor agent performance, engagement metrics, and behavioral drift in production Must Have - 5+ years of backend or ML engineering experience, with hands-on work deploying AI/ML models to production - Experience with LLM fine-tuning, prompt engineering, and inference optimization - Strong proficiency in Python; experience with Golang is a plus - Experience building and operating API services and data pipelines at scale - Familiarity with reinforcement learning techniques (RLHF, reward modeling) or agent framework development - Understanding of multi-model architectures — orchestrating multiple models or agents to collaborate on tasks - Experience deploying applications that integrate with third-party platforms and APIs (social media, messaging, commerce) - Strong systems thinking — comfortable reasoning about distributed systems, scaling, and reliability - Exceptional problem-solving and communication skills Nice to Have - Experience with character AI, conversational AI, or NPC behavior systems - Background in gaming, entertainment, or media technology - Experience with multi-modal AI (voice, image, video generation or processing) - Familiarity with blockchain infrastructure, on-chain payments, or crypto-native commerce - Experience with MCP (Model Context Protocol) or agent-to-agent communication protocols - Understanding of content safety, trust & safety systems, or responsible AI practices - Contributions to open-source AI/ML projects Benefits - Work remotely from anywhere in the world - Work at the intersection of AI, entertainment, and crypto - Flexible working hours - Flexible vacation policy - Competitive salary - Stock options - Full benefits* *Medical, Dental, Vision, and 401k retirement plans for US employees only.