Senior Product Software Engineer - AI Native

• Education (Required): Bachelor's degree in a relevant field, or equivalent demonstrated experience.
• Work Experience (Required): 4-6 years of relevant experience in product software engineering.
• Strong experience as a Full Stack Software Engineer
• Advanced knowledge of:
  • C#,.NET Framework,.NET Core, ASP.NET Web API
  • Angular
  • RESTful APIs and microservices architectures
  • SQL and relational database design
  • Redis
  • Asynchronous messaging systems (RabbitMQ, MassTransit, Azure Service Bus)
• Solid understanding of:
  • OOP, SOLID principles
  • Domain-Driven Design (DDD)
  • Clean Code practices

We are currently looking for a Senior Product Software Engineer - AI Native to join our team.

The Senior Product Software Engineer (AI native) will own software solutions from discovery through production, translating customer and business needs into secure, reliable, scalable, and maintainable software products. Takes ambiguous team-level problems and works across stakeholders to define requirements, shape solution approaches, coordinate dependencies, and deliver successful outcomes. Exercises sound engineering judgment and uses approved AI-assisted development and automation tools where appropriate, while remaining accountable for the quality, correctness, security, and operational readiness of all work approved or released. Serves as a technical resource within the team and helps strengthen the capability of less experienced engineers.

• End-to-End Solution Ownership:Own software solutions from discovery through production, translating customer, business, and technical requirements into sustainable outcomes.
• Problem Definition and Solution Design:Analyze ambiguous requirements, clarify objectives, evaluate alternatives, and determine solution strategies.
• Software Development:Design, develop, test, implement, maintain, and improve software applications, services, and platforms using direct and approved AI-assisted approaches.
• AI-Assisted Engineering:Apply approved AI-assisted techniques while validating outputs and maintaining accountability for architecture, quality, security, and operational readiness.
• Specification Development:Document requirements, design approaches, acceptance criteria, dependencies, risks, and implementation considerations.
• Verification and Validation:Determine and execute testing and verification approaches based on complexity, risk, and business impact.
• Technical Guidance:Provide technical guidance through design reviews, code reviews, knowledge sharing, mentoring, and engineering best practices.
• Troubleshooting and Production Support:Diagnose and resolve cross-component, production, and system-level issues and implement sustainable corrective actions.
• Security and Compliance:Apply security, privacy, compliance, and data-protection requirements throughout the lifecycle.
• Product and Stakeholder Collaboration:Partner with product, design, architecture, operations, security, and business stakeholders to shape solutions.
• Performance and Reliability Improvement:Improve performance, scalability, reliability, resiliency, and maintainability while reducing operational risk.
• Continuous Improvement:Improve engineering processes, delivery effectiveness, and team capability through automation, standardization, and knowledge sharing.
• Customer and Business Awareness:Consider customer impact, product strategy, operational implications, and business outcomes in technical decisions.

• Applies specification-driven and evaluation-driven approaches to ambiguous work. Provides context, constraints, and evaluation criteria, validates generated output, and coaches others on appropriate responsible use.

Every engineer remains accountable for the work approved or released, regardless of whether the work was produced directly or with approved AI assistance. AI-generated output remains unverified input until it has been reviewed and validated through practices appropriate to the associated risk.

• Determine the testing and validation needed within the assigned area.
• Ensure solutions meet quality, reliability, security, compliance, and operational expectations.
• Define and evaluate acceptance criteria.
• Produce appropriate evidence of readiness for production use.
• Identify, manage, and escalate technical risks.

• Software Engineering:The ability to design, develop, and maintain software systems and applications by applying principles and techniques of computer science, engineering, and mathematical analysis. This includes the capacity to understand user requirements, create and test software, and resolve software-related issues.
• Software Development:The ability to produce working software that meets its requirements, whether by writing it, directing approved tools that generate it, or reviewing and taking accountability for contributed work. Includes designing, testing, implementing, and the ability to read, trace, and diagnose code regardless of who or what produced it.
• Programming:Ability to build software that achieves intended outcomes by writing code, directing approved code-generation tools, or both. Includes the ability to interpret specifications, apply specification-driven development (SDD) practices, and read, trace, test, debug, and improve code regardless of its origin.
• Specification Driven Development: The ability to translate customer needs, business objectives, and product intent into clear, structured, and testable specifications that guide design, engineering, validation, and delivery. Uses specifications as the primary mechanism to align stakeholders, reduce ambiguity, enable AI-assisted development, and ensure solutions deliver the intended customer and business outcomes.
• Problem Solving:The ability to understand a complex situation or issue and devise a solution by defining the problem, identifying potential strategies, and choosing and implementing the most effective course of action.
• Analysis:The ability to examine complex situations or problems, break them into smaller parts, and understand how the parts work together.
• Testing:The skill of evaluating a system or process to identify problems, measure effectiveness, and ensure quality or functionality.
• Agile:The ability to respond to change swiftly and effectively, with emphasis on continuous improvement, adaptive planning, and flexibility.
• Version Control:The ability to work effectively in a shared codebase using branching, review, and merge practices. Includes writing reviewable changes, reviewing others' work, maintaining a clean history, and using the repository as a record of why a system is the way it is.
• Relational Database:The ability to design, implement, and manipulate relational databases, including proficiency in SQL, database management systems, and database design principles.
• APIs:The ability to design, develop, and manage application programming interfaces, including documentation, security, and testing practices that enable software systems to communicate.
• Design:The ability to structure software so it is understandable, changeable, secure, operable, and fit for the problem. Includes decomposing systems, defining interfaces and contracts, selecting appropriate patterns, and designing for testability.
• Framework:The ability to understand, use, design, and develop complex structures and systems in programming and related engineering contexts.
• Documentation:The ability to create, organize, maintain, and communicate technical information so it is current, accessible, and useful.
• Application Security Principles:The ability to apply application security controls that prevent, detect, and respond to vulnerabilities, including secure coding, access control, authentication, and security testing.
• Advanced Technology Adoption and Utilization:Applies specification-driven and evaluation-driven approaches to ambiguous work. Provides context, constraints, and evaluation criteria, validates generated output, and coaches others on appropriate responsible use.
• Ownership Mindset:Acts like an owner, takes accountability for outcomes beyond formal role boundaries, and creates disproportionate business value while respecting security, regulatory, financial, and delegated decision-making controls.
• Organizational Velocity:Increases speed by removing friction, simplifying decisions, and accelerating execution.
• Technical Innovation & Leverage:Creates innovative solutions whose impact far exceeds the effort required to build them.
• Engineering Leverage:Improves engineering productivity through tools, platforms, frameworks, and automation.
• AI-Native Thinking:Reimagines work and business models through AI-first thinking.
• Learning Velocity:Rapidly acquires and applies new capabilities ahead of business needs.
• Constructive Disruption:Improves outcomes by challenging assumptions with evidence, pragmatism, and low ego.
• Product & Market Instinct:Balances technology decisions with customer, market, and business realities.
• Talent Multiplier:Raises standards, attracts talent, develops future leaders, and builds excellence cultures.
• Enterprise Mindset:Optimizes for enterprise outcomes rather than local team wins.
• Strategic Judgment:Makes high-quality prioritization decisions, especially what not to pursue.
• Courage to Take Smart Risks:Balances potential risk with impact, encourages thoughtful experimentation, and facilitates learning from responsible risk-taking.
• Inclusive Collaboration:Models inclusion and collaboration by involving team members in decision making, respecting colleagues, and partnering across organizational boundaries.
• Inspiration & Alignment:Motivates achievement by aligning individual, team, and organizational goals.
• Analytical Skills:Takes a logical and systematic approach to solving problems and contributes quality alternative solutions that add value for the customer.
• Functional Expertise:Maintains and applies current knowledge of relevant practices and science in the field of expertise.
• Operational Expertise:Identifies opportunities for improved operational efficiency, applies quality standards, and ensures accuracy and completeness of work outputs.