Role Description We are looking for a client-obsessed AI Solutions Engineer with entrepreneurial tendencies to join the good fight and help build out our Solutions Engineering org. You’ll be the trusted technical advisor for our customers, driving business value, offering advice, and growing accounts. You’ll accomplish this by: - Working closely with some of the most sophisticated ML / GenAI teams in the world. - Acting as a trusted advisor to our customers, while also building relationships with technical and business stakeholders. - Advising on GenAI and ML best practices. - Giving ML and LLM product demos to technical and business stakeholders. - Running strategic business reviews for customers in partnership with our sales team. - Interfacing with our pre-sales engineering team to gather client goals and KPIs. - Partnering with our product and engineering teams to help drive the product roadmap. - Spearheading new opportunities within existing accounts to help drive expansions. Qualifications - Previous experience working as a Data Scientist, Machine Learning Engineer, or as an Engineer working with ML models or GenAI applications in production. - Comfortable working in public Cloud environments (AWS, Azure, GCP). - Knowledge of machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn. - Knowledge of LLM / Agentic frameworks such as Llamaindex, LangGraph, and DSPy. - Understanding of ML/DS concepts, model evaluation strategies, and lifecycle (feature generation, model training, model deployment, batch and real-time scoring via REST APIs) and engineering considerations. - Understanding of GenAI concepts and application evaluation + development lifecycle. - Proficiency in a programming language (Python, JS/TS, Java, Go, etc). - Strong communication skills - ability to simplify complex, technical concepts. - A quick and self-learner - undaunted by technical complexity of production ML deployments and welcome the challenge to learn about them and develop your own POV. Requirements - Previous engineering experience in: - Data Science - MLOps - ML Frameworks - LLM / Agentic frameworks - Customer-facing experience strongly preferred such as Solutions Architect, Implementation Specialist, Sales Engineer, Customer Success Engineer, Consultant, or Professional Service roles. - Prior experience working with applications deployed with Kubernetes. - Prior experience demoing technical products to both business and technical audiences. Benefits - The estimated annual salary and variable compensation for this role is between $125,000 - $175,000, plus a competitive equity package. - Total compensation includes a comprehensive benefit package, including: - Medical, dental, vision - 401(k) plan - Unlimited paid time off - Generous parental leave plan - Others for mental and wellness support - WFH monthly stipend to pay for co-working spaces for remote employees.

• Engineer solutions for real problems - Understand, design, and deploy solutions using AI/ML models for real problems • Own platform stability and maintenance – Manage the deployment lifecycle of new features and fixes, ensure the platform is stable, scalable, and has good observability. • Craft high-quality code – Produce clear and efficient code, actively engage in code reviews, and champion technological excellence. • Write tech specs – Translate business needs into creative and technically robust solutions. • Engage with cross-functional partners – Collaborate closely with product and stakeholders to drive the success of the project. • Document thoroughly – Create comprehensive technical documentation, simplifying the onboarding process for newcomers. • Refine best practices – Proactively seek and implement improvements in engineering and organizational workflows. • Drive engineering planning and processes - Participate in sprint planning, effort estimation, capacity planning, and release management. • Architect and evolve the overall stack - Guide technology decisions and stack improvements; anticipate scaling challenges.

• Lead AI/ML innovations - from coming up with ideas for research, POCs, to contributing to full product feature build out. • Act as GenAI expert - live and breathe AI field news and trends. • Hands-on work trying out new concepts, AI frameworks and tooling around them for different use cases - context management, memory, retrieval, agentic loop, A2A and many other solutions. • Be part of product development - from discovery together with PMs to delivery within cross-functional teams. • Evaluate new LLMs (proprietary and OSS), and assess for performance, cost, and other metrics. • Bonus: act as AI evangelist to promote the adoption and understanding of AI both within and outside nexos.ai.

• Liderar o time de desenvolvimento de IA • Orquestrar a comunicação entre stakeholders e time de executores • Gerenciar backlog de produto e priorização estratégica • Promover um ambiente de alta performance • Atuar na intervenção de impedimentos e fomentar feedback contínuo • Colaborar com diferentes agentes autônomos