- Implement agentic AI workflows for clinical source verification, discrepancy detection, and intelligent query generation.
- Build and integrate LLM-powered agents using AWS Bedrock + open-source frameworks (LangChain, AutoGen).
- Develop event-driven pipelines with AWS Lambda, Step Functions, and EventBridge.
- Optimize prompt engineering, retrieval-augmented generation (RAG), and multi-agent communication.
- Integrate AI agents with external systems through secure APIs.
- Experience in healthcare/Life Sciences AI solutions with regulatory compliance preferred
- Collaborate with data engineers for PHI/PII-safe ingestion pipelines.
- Monitor, test, and fine-tune AI workflows for accuracy, latency, and compliance.
Qualifications
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- Bachelor's in Computer Science, Engineering, or related field.
- 5-6 years in AI/ML engineering with hands-on LLM/agentic AI development.
- Strong coding skills in Python/TypeScript and experience with LangChain, LlamaIndex, or AutoGen.
- Familiarity with AWS AI services (Bedrock, SageMaker, Textract, Comprehend Medical).
- Experience in API integrations and event-driven architectures.
- Experience in healthcare/Life Sciences AI solutions with regulatory compliance preferred
- Problem-solving mindset with ability to experiment and iterate quickly.