Senior Software Engineer / Senior AI Engineer

In this role you will build and ship AI-powered features within an Embedded Innovation Squad, turning internal AI experiments into production-ready solutions. You’ll define technical direction, develop tool use and retrieval pipelines, and integrate AI into enterprise workflows. You’ll maintain quality, observability, and guardrails while collaborating with host-function stakeholders to fit real-world needs. This role offers hands-on autonomy, a path to reusable patterns, and opportunities to influence how AI accelerates customer value.

• Comprehensive Pension Plan
• Generous vacation entitlement and sabbatical option
• Maternity, Paternity, Adoption and Family Care leave
• Personal Choice budget
• Internal communities and networks
• Employee discounts

• Build prototypes and proofs of concept, delivering agentic AI solutions to production standards
• Design and test tool use, retrieval pipelines, and agent workflows
• Contribute to evaluation, observability, and guardrails for agentic systems
• Integrate AI capabilities into existing enterprise workflows and systems
• Maintain high code quality and documentation for reuse
• Identify and flag technical risks and blockers early
• Collaborate with peers to finalize requirements and fix moderately complex bugs
• Create reusable patterns, reference implementations, and starter kits for cross-team reuse
• Instrument solutions to measure outcomes against baselines
• Engage with host-function stakeholders to ensure alignment with real workflows
• Support handover and capability-building so solutions remain operable after the squad moves on
• Keep abreast of new technology developments
• Take on related responsibilities as the squad evolves

• 3+ years of software engineering experience with hands-on building of LLM-powered applications in production (RAG, tool-augmented agents, or agentic workflows)
• BS in Engineering, Computer Science, or equivalent
• Ability to work autonomously and deliver in time-boxed cycles with validated outcomes
• Experience with building RAG pipelines, tool-augmented agents, and agentic workflows; familiarity with prompt engineering, context management, evaluation, and observability
• Understanding of agent memory, tools, and retrieval for multi-step tasks
• Enterprise integration via APIs and data pipelines
• Cloud experience with AWS, Azure or GCP
• Delivery practices including CI/CD, modern SDLC, TDD and code review
• Experience with relational, columnar and vector stores and solid data modeling principles
• Languages: Python, Java, TypeScript/JavaScript, SQL and relevant AI SDKs
• Strong written and verbal communication with technical peers and stakeholders
• Ability to instrument solutions to capture usage, productivity and quality metrics against baselines

• autonomy
• clear communication
• problem-solving
• LLM-powered applications
• RAG pipelines
• tool-augmented agents