Responsibilities
- Build GenAI apps: Implement LLM-based features such as Q&A, summarization, extraction, and classification using prompt engineering and structured outputs.
- Implement RAG pipelines: Build ingestion + chunking + embeddings + retrieval flows using vector databases to ground answers in enterprise knowledge.
- Develop agent workflows: Create agentic automations (tool calling, task routing, multi-step workflows) using common frameworks/patterns.
- Enterprise integration: Integrate AI services with internal systems via APIs, auth, and approved access controls; follow enterprise engineering standards.
- Quality & monitoring: Add logging/telemetry and participate in evaluation/testing to catch regressions and ensure stable production behavior.
- Developer productivity: Use coding assistants (e.g., Copilot/Cursor-style) to accelerate development while maintaining clean code and reviews.
Requirements
- 5+ years total IT/software engineering experience (enterprise applications, APIs, services).
- 2+ years hands-on AI/ML/GenAI experience, including building or supporting AI solutions beyond
Nice to Have
- Familiarity with FastAPI/Flask, Docker, CI/CD, and production monitoring patterns.
- Exposure to financial services or regulated data environments.
Work Arrangement
Hybrid
