1458 Data Scientist

ROLE & RESPONSIBILITIES:

• Define and build agentic system architectures that leverage Amazon Bedrock AgentCore and agent frameworks
• to enable multi-step reasoning and automated workflows.
• Lead technical strategy for model selection, fine-tuning, and inference, advising on cost vs. performance tradeoffs.
• Design and implement containerized deployment standards using Docker and Kubernetes to ensure consistent,
• scalable, and fault-tolerant ML operations.
• Architect secure, low-latency networking for model-to-service and service-to-service communication across
• private and public networks.
• Perform systems-level performance engineering: select appropriate compute accelerators, run load and stress
• tests, and conduct capacity planning for production readiness.
• Establish and operate MLOps and GenAI Ops practices, including CI/CD pipelines, model versioning, and deployment automation.
• Implement observability, logging, monitoring, and incident response for production AI systems to ensure operational excellence.
• Own end-to-end system design for AI workloads: data pipelines, model training, inference, orchestration, and lifecycle management.
• Integrate foundation models into enterprise RAG and tool-use pipelines, enabling complex, real-world use cases.
• Provide technical leadership and mentorship to engineers and stakeholders on architecture, best practices, and operational standards.

QUALIFICATIONS/EXPERIENCE:

• Appropriate academic qualification such as Computer Science, Engineering or Statistics
• Demonstrated track record delivering large-scale AI solutions for enterprise customers, including end-to-end
• ownership of architecture, operations, and stakeholder engagement

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