Lead the design and implementation of agentic AI systems on AWS, orchestrating multi-agent workflows for clinical data and ensuring compliance in healthcare/Life Sciences. Mentor teams, collaborate with stakeholders, and drive technical solutions using cloud-native AI services and agent frameworks.
Responsibilities:
Lead architecture design for agentic AI systems leveraging AWS services (Bedrock, SageMaker, Lambda, Step Functions, EventBridge, OpenSearch, DynamoDB).
Define multi-agent orchestration strategies for clinical data workflows, source verification, and discrepancy resolution.
Evaluate trade-offs between LLM-based reasoning, rules engines, and hybrid AI architectures.
Experience in healthcare/Life Sciences AI solutions with regulatory compliance preferred
Mentor AI engineers, developers, and DevOps teams on agent-based architectures.
Collaborate with clinical SMEs, compliance teams, and sponsors to align technical design with business outcomes.
Qualifications:
Bachelor’s/Master’s in Computer Science, AI, or related field (PhD a plus).
8–12 years in AI/ML engineering with 3–5 years in cloud-native AI architecture.
Hands-on experience with AWS AI stack (Bedrock, SageMaker, Comprehend Medical, Textract, Kendra, Lex).
Strong understanding of agentic AI frameworks (LangChain, LlamaIndex, Haystack, CrewAI, AutoGen).
Proven expertise in workflow orchestration (Step Functions, Airflow, Temporal) and multi-agent pipelines.
Experience in healthcare/Life Sciences AI solutions with regulatory compliance.
Strong leadership, stakeholder communication, and solution governance skills.
AWS Bedrock, SageMaker, Lambda, Step Functions, EventBridge, OpenSearch, DynamoDB, Comprehend Medical, Textract, Kendra, Lex, LangChain, LlamaIndex, Haystack, CrewAI, AutoGen, Step Functions, Airflow, Temporal, AI/ML Engineering, Cloud-Native AI Architecture, Multi-Agent Pipeline Orchestration, Healthcare AI, Regulatory Compliance, Team Leadership, Stakeholder Communication, Solution Governance