#### About the role: 

We're seeking a Generative AI Engineer with deep experience orchestrating chain-like LLM workflows involving multiple interdependent tasks, agents, and external toolchains. Your systems should show robust sequential logic, checkpointing, error handling, and task memory management.


#### Requirements:

- 8+ years of experience in software engineering with advanced Python
- Direct experience  engineering strong, deterministic prompts
- Designing agent sequences or pipelines for structured data flows
- Familiarity with LangChain Expression Language (LCEL), TaskGraph, or similar sequential LLM frameworks
- Strong debugging skills for tracing agent state, outputs, misrouting, and logical breakdowns across multistage workflows
- Experience integrating and managing workflows with AWS Bedrock (Claude, Titan, Bedrock Prompt Management, etc.)
- Skilled in Docker-based deployments, GitHub CI/CD pipelines, and .env-based environment isolation in GCP and AWS
- Proven ability to manage memory, agent state persistence, and error recovery in long-running LLM workflows
- Experience with long-context prompt management, summarization, and multi-stage chaining logic
- Demonstrated integration experience with external APIs and knowledge sources (e.g., MedPaLM, PubMed, UpToDate)
- Proficient with markdown-based sectioning, intermediate checkpoint saving, and stateful reruns
- Experience implementing staged pipelines with persistent folders and resumable execution flows
- Skilled in integrating patient intake forms and user-defined health concerns into prompt generation logic, with support for custom temperature/language settings
- Familiarity with LaTeX post-processing, citation formatting, and automated error mitigation for LLM-generated documents
- Experience building robust error-handling and retry systems for LLM pipelines (e.g., failed chapter generation, invalid citations, broken biomarker references)
- Demonstrated experience with prompt benchmarking, hallucination mitigation, and outline compliance across Claude, Gemini, o1/o3-mini, and other models
- Hands-on experience deploying and managing LLM pipelines on Google Cloud Platform (Cloud Run, Firestore, Cloud Functions, batch jobs)

#### Ideal to Also Have:

- Background in healthcare informatics or structured clinical data
- Experience deploying models via AWS Batch, with attention to permissions, logging, and cost optimization
- Strong documentation and sprint-tracking habits using Notion or ClickUp
- Experience contributing to robust, test-driven PR-based engineering workflows