Senior Solutions Engineer (AI)

Location: New York City, United States; hybrid work with attendance at the NYC office at least three days per week

Salary: $200,000–$265,000 NYC OTE, plus equity

Company

hirify.global provides an Agentic Decision Platform that enables financial institutions to combine AI agents, rules, contextual data, and human oversight to automate and optimize credit, risk, fraud, and insurance decisions.

What you will do

• Translate customer priorities and problem statements into high-impact Agentic AI use cases throughout the enterprise sales cycle.
• Lead proof-of-value projects combining customer data, ML/AI models, and the hirify.global platform to demonstrate strategic ROI.
• Build technical prototypes, demos, integrations, and workflows that simulate production environments.
• Collaborate with customers and partners to design solutions on hirify.global’s Agentic Decisioning Platform.
• Present implementation value and strategic roadmaps to senior stakeholders, including C-level executives.
• Share prospect insights and competitive intelligence with Product, and create reusable tools and best practices for the Solutions Engineering function.

Requirements

• Hybrid availability in New York City with office attendance at least three days per week.
• 3+ years of hands-on experience writing production Python code.
• Experience navigating complex enterprise organizations and working with technical and business stakeholders.
• Experience planning and managing technical evaluations, proof-of-concept projects, and multiple high-stakes prospects.
• Proven experience supporting high-contract-value enterprise deals through discovery, strategic positioning, and technical objection handling.
• Strong customer-centric, collaborative, and growth-oriented approach.

Nice to have

• 3–5 years of enterprise Solutions Engineering, Sales Engineering, or senior technical consulting experience in B2B SaaS.
• Understanding of REST API design, integration, and authentication.
• Experience building, tuning, or deploying production ML models with tools such as scikit-learn, XGBoost, PyTorch, or TensorFlow.
• Experience with AI use cases or domain expertise in financial services, including fraud detection, credit scoring, underwriting, banking, insurance, capital markets, or payments.
• Ability to explain model behavior to both technical stakeholders and financial-services domain experts.

Culture & Benefits

• Early-stage environment with a flat hierarchy and direct communication with founding team members.
• Mentorship, professional development opportunities, and a self-development budget for conferences, books, or classes.
• Top-of-market cash and equity compensation.
• Choice of equipment and meaningful home-office setup.
• Inclusive workplace focused on collaboration, growth, and challenging the status quo.