Back to SearchLead AI/ML Engineer
Remote in Argentina, & 3 others
AI Solution Engineering& 8 others
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We are seeking a Lead AI/ML Engineer to drive the migration of patient predictive models into a new analytical rework framework, architect scalable ML solutions on AWS, and lead the implementation of agentic workflows powered by GenAI and LLM capabilities. This role combines hands-on technical leadership with team enablement, guiding the transition of critical healthcare models while ensuring data integrity, operational continuity, and adoption across the organization.
Responsibilities
- Design and develop the migration solution for existing patient predictive models into the new analytical rework framework
- Architect the model transition approach to ensure data integrity and prediction accuracy
- Implement MLOps practices for model lifecycle management and monitoring
- Execute the transition of all in-scope patient models to AWS-native ML services
- Validate migrated models against baseline performance metrics
- Ensure operational continuity throughout the migration process
- Design and implement agentic and multi-agent workflow patterns
- Integrate GenAI and LLM capabilities into the analytical framework
- Develop automation for model training, evaluation, and deployment pipelines
- Lead team onboarding sessions for the new analytical framework
- Conduct comprehensive knowledge transfer sessions to support adoption
- Create documentation and training materials for operational handoff
Requirements
- 5+ years of experience in AI/ML engineering with strong expertise in Amazon Web Services (AWS)
- Deep background in data engineering
- Extensive skills in MLOps
- Strong qualifications in agentic workflows and multi-agent systems
- Solid expertise in Large Language Models (LLM) and GenAI
- High proficiency in Python
- English proficiency at B2 level or higher
Nice to have
- Familiarity with Amazon SageMaker
- Familiarity with AWS Bedrock
- Background in healthcare domain
- Experience in patient data analytics and predictive modeling