Senior Software Engineer

Who you are

• 7+ years in software engineering, with strong proficiency in Python, its eco-system and tooling
• Experience building and deploying GenAI-based applications
• Proficient with AI-assisted Engineering (we use Claude Code)
• Solid experience building and maintaining data-driven Web APIs on any cloud platform
• Strong fundamentals of software architecture and system design
• Experience with testing strategies (unit, integration, E2E) and a quality-first mindset
• Excellent communication and collaboration skills
• Experience building and deploying agentic applications in production
• Experience in the audit domain or fintech
• Experience with document processing technologies
• Experience with observability tools

What the job involves

• We are looking for a Senior Software Engineer to join the AI Agents Team at DataSnipper. You will build the intelligence layer that orchestrates the full assurance lifecycle from risk assessment to control testing, evidence gathering, and documentation generation, directly reducing manual effort and accelerating audit outcomes for our customers
• This is a hands-on engineering role in a high-caliber team that moves fast and holds a high bar. You will design and implement AI-powered agents, drive architectural decisions and help shape the future of automated assurance
• Design and implement AI-powered agents that automate assurance workflows, analyzing Excel files and documents using natural language commands
• Build and maintain production APIs and services in a Python monorepo (FastAPI, Pydantic AI, MongoDB)
• Integrate with Azure and its AI services for document intelligence, blob storage, and cloud infrastructure
• Contribute to the React chat interface that provides an interactive experience for users
• Implement security controls aligned with OWASP GenAI security protocols
• Participate in an on-call rotation (second line after SRE and tech support)
• Architect services and systems using well-established design principles, anticipating future use cases
• Produce robust, production-ready code that anticipates edge cases; review for correctness, design quality, and maintainability
• Raise the engineering bar through technical mentorship, high-quality code reviews, and establishing best practices
• Use quality metrics to identify gaps and recommend improvements aligned with testing frameworks
• Diagnose complex issues across services and use operational data (MLflow, Grafana) to drive stability improvements
• Break down epics, align team dependencies, and manage ambiguity for the team
• Design solutions with product partners, refine team roadmap, and simplify technical approaches
• Foster a culture of documentation and knowledge sharing within the team and with business stakeholders
• Integrate AI tools into workflows, critically evaluate AI outputs, and contribute to team-level AI adoption