AI Solutions Engineer

Build and Scale AI Solutions

1. Design and deliver production-grade AI-enabled applications using modern full-stack and cloud native patterns
2. Build reusable AI frameworks and reference implementations (e.g., RAG, document processing, agent/workflow patterns)

3. Integrate AI into enterprise platforms and workflows with strong engineering discipline (clean code, automation, observability, reliability) Apply AI with an AI-first mindset

4. Use AI to accelerate delivery, reduce friction, and scale outcomes
5. Implement applied GenAI patterns including RAG, prompt/tool orchestration, agentic workflows, and evaluation with guardrails

6. Design model-agnostic solutions resilient to rapid AI ecosystem change Enable Teams and Own Engineering Excellence

7. Turn complex AI implementations into simple, repeatable patterns
8. Mentor engineers; lead architecture and design reviews to raise quality and consistency
9. Partner with stakeholders on requirements and shippable milestones
10. Own DevOps hygiene (CI/CD, automated testing, telemetry, monitoring) and drive continuous improvement

1. AI-first builder mindset, designing reusable solutions for scale and impact, with clear technical communication
2. 6+ years building and operating production full-stack systems at scale
3. Hands-on experience with distributed, cloud native architectures (APIs, data, event-driven systems)
4. Strong foundation in system design, scalability, resiliency, security, and observability
5. Hands-on, production experience building AI/GenAI powered applications, not just experimentation or POCs
6. Applied GenAI expertise including RAG, LLM integration/orchestration, prompt design, and evaluation/guardrails
7. Proficiency in Java and/or Python with modern frameworks (e.g., Spring Boot, Python services)
8. Experience with CI/CD, automated testing, and production observability

Desired Skills

1. Public cloud experience (Azure preferred)
2. Experience building internal platforms, frameworks, or developer tooling
3. Familiarity with vector databases, embeddings, Kafka, or high-volume messaging systems
4. Experience in regulated or financial services environments
5. Experience working with globally distributed engineering teams