Role Description We're seeking an AI Software Engineer II to help build the next generation of AI-powered capabilities. In this role, you'll develop and deliver production-ready software that integrates large language models (LLMs), machine learning, and cloud-based AI services into customer-facing applications. - Design, build, and deploy AI-powered features within our SaaS platform. - Integrate LLMs and cloud AI services into production applications. - Develop scalable backend services, REST APIs, and application integrations. - Build and maintain data pipelines that support AI workflows. - Contribute across the application stack, including backend services and frontend enhancements as needed. - Collaborate with Product Managers, UX Designers, and Software Engineers to deliver customer-centric solutions. - Rapidly prototype new AI capabilities and iterate on customer feedback. - Write clean, maintainable, well-tested code that follows software engineering best practices. - Optimize application performance, scalability, and reliability. - Participate in code reviews and contribute to architectural decisions. Qualifications - Bachelor’s degree in Computer Science, Software Engineering, or a related technical field (or equivalent practical experience). - 2–4 years of professional software engineering experience. - Experience developing applications with Python, C#, Java, JavaScript, or similar object-oriented languages. - Experience with consuming or integrating LLMs and AI services via APIs. - Experience building RESTful APIs and backend application services. - Working knowledge of cloud platforms, including AWS, Azure, or Google Cloud. - Experience with Git and modern software development practices. - Understanding of databases and data modeling (SQL and/or NoSQL). - Strong analytical and problem-solving skills. - Excellent collaboration and communication skills. Requirements - Experience with Retrieval-Augmented Generation (RAG). - Experience using vector databases and embeddings. - Familiarity with AI orchestration frameworks such as LangChain or LlamaIndex. - Experience building data pipelines and AI workflows. - Experience deploying containerized applications with Docker and Kubernetes. - Exposure to CI/CD pipelines and DevOps practices. - Experience developing customer-facing SaaS applications. Benefits - Competitive pay - Unrestricted PTO - Comprehensive benefits package, including health, dental, and vision insurance. - 401(k) plan with a company match. - Professional development opportunities and continuous training - A supportive, dynamic work environment with opportunities for growth and advancement. Company Description NCC is a leader in providing award-winning software, credit and compliance solutions, and cutting-edge data to the automotive industry for over 30 years. Our solutions help dealerships streamline their operations, enhance their finance and insurance (F&I) processes, and drive profitability. We are looking for individuals who thrive in a high-growth environment.

• Own Problems End-to-End: Take product problems from discovery to production with full ownership • User Understanding: Deeply understand user problems, workflows, and constraints to inform solutions • Fullstack Development: Design and build features across UI, backend, database, APIs, and integrations • AI Workflow Development: Build AI-powered workflows using LLMs, retrieval, agents, structured extraction, and tool use • Data Integration: Work with documents, data sources, and enterprise systems for comprehensive solutions • Rapid Iteration: Ship quickly, test with users, and iterate based on real feedback and usage patterns • Product Quality: Ensure quality across UX, performance, reliability, security, and maintainability • AI-Augmented Development: Use AI coding tools as part of daily workflow for accelerated delivery • Pattern Recognition: Turn repeated customer patterns into scalable product features

• Translate product needs into agentic tools and services and ship them to production. • Scale prototypes to large numbers of users. • Improve and test our semantic search service. • Build evaluation pipelines for code retrieval and coding task performance.

Role Description We are hiring a Senior Data Scientist / AI Engineer to design, build and scale production-grade AI solutions, with a strong focus on Generative AI, NLP, and healthcare-related use cases. This role is suited for a hands-on technical leader who can translate complex business and domain problems into reliable AI systems that create value, work closely with cross-functional teams, and mentor high-performing data science talent across multiple geographies. The ideal candidate brings strong machine learning fundamentals, deep experience with NLP and transformer-based models, practical exposure to LLMs and RAG systems, and the ability to deliver measurable business and operational impact in real-world environments. Key Responsibilities - Design, build, and deploy end-to-end AI and machine learning solutions, with a focus on GenAI, NLP, and healthcare applications. - Develop and productionize LLM-based workflows, including prompt engineering, evaluation frameworks, fine-tuning approaches, and Retrieval-Augmented Generation systems. - Translate ambiguous business and healthcare problems into structured data science solutions with clear success metrics. - Own the full model lifecycle, including data preparation, experimentation, validation, documentation and articulation of results, deployment, monitoring, and continuous improvement following RAI guidelines. - Work with large-scale structured and unstructured data, including clinical, operational, claims, member, provider, or other healthcare-related datasets. - Partner with product, engineering, business, clinical, and compliance stakeholders to ensure solutions are scalable, explainable, secure, and aligned with business needs. - Lead, mentor, and develop a team of data scientists, and AI engineers, setting high standards for technical quality, analytical rigor, and delivery discipline. - Drive best practices in model development, code quality, documentation, reproducibility, and responsible AI. Qualifications - 8+ years of experience in developing and implementing end-to-end solutions using Machine Learning and AI tools. - 5+ years of hands-on experience in NLP, deep learning, and transformer-based models. - 2+ years of practical experience building end-to-end Generative AI solutions, including LLM workflows, fine-tuning, evaluation, and RAG-based systems. - Strong proficiency in Python and PySpark is mandatory. - Proven experience building and deploying production-grade ML or AI systems at scale. - Strong business acumen with the ability to convert complex business problems into practical AI solutions that deliver measurable impact. - 2+ years of experience leading, mentoring, or managing high-performing data science teams is highly desirable. - Excellent written and verbal communication skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders. - Experience working in a matrix organization. Preferred Qualifications - Experience in the healthcare domain is highly desirable, especially in areas such as clinical AI, payer/provider analytics, population health, claims, care management, medical operations, or health data platforms. - Experience working with healthcare data standards, privacy requirements, regulated environments, or responsible AI considerations in healthcare. - Hands-on experience with Azure, Databricks, or equivalent cloud and data platforms. - Working knowledge of MLOps, CI/CD for ML, model monitoring, model governance, and scalable deployment patterns. - Experience optimizing AI systems for accuracy, performance, latency, reliability, cost, and maintainability. - Exposure to multimodal AI, knowledge graphs, medical text analytics, or clinical decision support use cases is a plus. What Good Looks Like - Strong technical depth combined with practical judgment. - Ability to operate in ambiguous environments and bring structure to complex problems. - High ownership mindset with a focus on outcomes, not just models. - Clear communication, strong stakeholder management, and the ability to influence across teams. - Passion for building AI solutions that are robust, responsible, and meaningful in a healthcare context.