• Work hands-on with engineering to write user stories, manage the backlog, drive sprint execution, and remove blockers. • Own and prioritize the roadmap for Cority’s AI platform in partnership with engineering and stakeholders across the business. Translate strategy into executable plans. • Build quick prototypes and run A/B tests to validate ideas before they enter the roadmap. • Be the connective tissue between engineering, product teams, and leadership. Keep everyone aligned, communicate progress clearly, and flag issues early. • Champion trust, security, and data governance as foundational platform requirements. Work with engineering to ensure these are built in, not bolted on. • Own the tools and processes that keep the team running, including Aha, Jira, sprint planning, and delivery tracking. • Partner with domain PMs to validate platform capabilities through prototypes and experiments, ensuring real product needs are met and use cases ship on time.

• Operate the SDLC natively with AI as part of the workflow. • Consistent use of automations with agents performing multi-step tasks. • Structure specifications that feed the AI and maintain quality control. • Operate securely in production.

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.