Senior AI Engineer

• Design, implement, and deploy ML/AI models end-to-end, from concept through production, including data pipelines, training workflows, and optimization for performance, accuracy, and efficiency
• Maintain and evolve AI systems in production, monitoring for drift, debugging issues, and driving ongoing improvements to reliability and scalability
• Use AI-forward coding tools such as Claude and Cursor to ship higher-quality work at pace
• Partner with product, engineering, and data teams to align AI work with broader product and business goals
• Translate technical tradeoffs, model behavior, and constraints into terms non-specialists can act on
• Participate actively in code reviews and design discussions, raising concerns and offering constructive feedback
• Contribute to AI architecture decisions with perspective on tradeoffs and long-term implications
• Take ownership of meaningful work end-to-end, including getting AI into production
• Raise engineering practices through individual contributions and by supporting more junior engineers

• 4+ years professional software engineering experience, with 2+ years focused on AI/ML systems in production and hands-on experience with generative AI development
• Strong software engineering background (Python or similar)
• Working knowledge of cloud platforms, preferably with in-depth understanding of AWS services and AWS GenAI offerings
• Proven ability to design and ship agentic systems
• Experience with AI frameworks and orchestration tools
• Experience with evaluation frameworks and observability tools for LLM apps
• Understanding of AI safety, responsible AI principles, prompt injection defenses, and PII handling
• Hands-on experience building RAG pipelines: chunking strategies, embedding models, vector databases
• API development experience, including designing and integrating with internal and third-party services
• Cost optimization expertise: token economics, caching strategies, model routing, quantization
• Working knowledge of Docker and Kubernetes for containerized deployments
• Demonstrable, day-to-day usage and knowledge of AI-forward coding tools such as Claude Code and Cursor
• Strong problem-solving skills and ability to navigate ambiguous technical challenges with sound judgment

• Specialized training through the Advanced AWS Partnership and AWS Patterns Partnership
• Early access to emerging cloud capabilities
• Access to advanced engineering resources
• Accelerated professional growth
• Hands-on exposure to complex problem-solving
• Collaboration with experienced architects and senior engineers
• Work within an elite global engineering ecosystem