• Lead early-phase AI systems: Design and build AI-first user experiences from the ground up, pairing rapid experimentation with strong measurement loops to evaluate performance, relevance, safety, and cost in production. • Influence product direction: Bring an engineering-driven perspective to what we build, how we build it, and which tradeoffs are worth making at early stages—charting a multi-step path that turns quick wins into durable, scalable capabilities. • Design and build scalable AI systems: Once direction is validated, evolve prototypes into reliable, maintainable, and observable production systems, balancing iteration speed with long-term platform health. • Build AI-powered product experiences: Develop AI-assisted modeling and authoring workflows to accelerate plan design, data mapping, and explanation for customers. • Deliver retrieval-augmented insights: Implement contextual help, explainers, and policy Q&A grounded in customer data and system metadata using robust retrieval-augmented generation patterns. • Own guardrails and evaluation: Design and implement evaluation and safety primitives—including offline and online evals, telemetry, human-in-the-loop workflows, safety filters, and red-teaming—to ensure trustworthy AI behavior. • Create reusable AI platform components: Build shared infrastructure for prompt orchestration, hybrid retrieval, caching, tracing, evaluation, and cost/performance controls that enable teams to ship AI features safely and efficiently. • Raise the technical bar: Contribute to architecture, coding standards, testing strategy, and mentoring; promote best practices across AI systems, platform design, and production readiness.

• Research, design, and develop innovative generative AI models and algorithms. • Implement and optimize deep learning architectures for generative tasks, focused on text generation. • Collaborate with cross-functional teams to define goals, requirements, and deliverables and implement the features. • Train and fine-tune models using large-scale datasets and advanced techniques. • Evaluate and assess model performance, making necessary adjustments to improve results. • Stay up-to-date with the latest advancements in generative AI trends and tools and contribute to the team's knowledge base. • Write clean, efficient, and maintainable code, following best practices and coding standards. • Document research findings, methodologies, and technical specifications. • Participate in code reviews and provide constructive feedback to peers. • Contribute to the development of tools and frameworks to facilitate generative AI research and development. • Interpret insights from our pre-sales, sales, customer success teams and clients to create AI-based features.

• Design, develop, and maintain backend systems in Python, integrating AI-driven functionalities and APIs • Implement prompt engineering, Retrieval-Augmented Generation (RAGs), and basic generative AI solutions into platform workflows • Collaborate on AI model deployment, fine-tuning, and production integration • Apply ML algorithms such as linear regression, logistic regression, tree-based methods, and neural networks where applicable • Ensure scalable, secure architecture leveraging AWS services (EC2, S3, Lambda, etc.) • Contribute to architectural decisions, backend infrastructure, and system optimization • Work cross-functionally with product, data science, and engineering teams to align AI initiatives with business goals • Mentor junior AI engineers as the team grows • Stay current with emerging AI technologies and assess their applicability to product needs • Business travel necessary, especially for Remote candidates

• Participate in pre-sales phase to show company expertise, provide technical design and estimations; • Work collaboratively with the data science team, backend engineer, devops, focusing on understanding business challenges and identifying potential tech-based solutions. • Coach and lead ML Engineers; • Develop and assess data-driven models with a focus on Natural Language Processing (NLP) and generative AI technologies. • Utilize advanced techniques such as Large Language Models (LLM), Retrieval Augmentation Generation (RAG), and semantic vectors to enhance model performance. • Collaborate with a team and client to ensure prompt alignment with project goals and user needs; • Monitor and analyze performance to identify areas for improvement; • Optimize AI prompt generation process to enhance overall system performance; • Stay up-to-date on the latest advancements in AI, natural language processing and machine learning; • Provide support to teams in understanding prompt engineering best practices; • Draft and distribute reports on prompt performance and development progress