1458 Data Scientist

SUMMARY:
ESSENTIAL SKILLS:

• Proven experience designing and building agentic system architectures using Amazon Bedrock AgentCore and
• agent frameworks (e.g., LangChain, LangGraph, Strands Agents).
• Strong expertise in orchestrating multi-step reasoning, tool invocation, state management, and workflow
• automation for AI agents.
• Deep hands-on knowledge of training and deploying models with PyTorch and TensorFlow.
• Experience defining model strategy, including architecture selection, fine-tuning approaches, inference
• patterns, and cost/performance trade-offs.
• Containerization and orchestration skills: Docker and Kubernetes for scalable, fault-tolerant ML/GenAI
• deployments.
• Solid understanding of networking for ML workloads, including VPC design, ingress/egress, private and
• internet-facing communication patterns, and low-latency design.
• Systems-level performance engineering: selecting CPU/GPU/accelerator hardware, plus experience with load
• testing, stress testing, and capacity planning for ML systems.
• MLOps and GenAI Ops experience: CI/CD for models, model versioning, observability, logging, monitoring, and incident response practices.
• Strong software engineering skills in Python and familiarity with building robust, production-ready APIs and back-end services.
• Experience integrating foundation models with Retrieval-Augmented Generation (RAG) pipelines, tool use, and agentic workflows for enterprise use cases.

• Prior experience working with Amazon Bedrock and other cloud-managed foundation model services.
• Familiarity with LangChain extensions, LangGraph, Strands Agents or similar orchestration toolkits at scale.
• Experience with infrastructure-as-code tools (e.g., Terraform, Terragrunt) for reproducible cloud infrastructure.
• Knowledge of serverless components (Lambda, Step Functions, EventBridge) for orchestration and eventdriven
• workflows.
• Background in secure cloud architectures, IAM best practices, and security hardening for AI platforms.
• Experience with data engineering and building reliable ETL/data pipelines for model training and feature stores.
• Familiarity with observability stacks (Prometheus, Grafana, CloudWatch) and distributed tracing for ML
• services.
• Experience optimizing inference costs through batching, quantization, and model distillation techniques.
• Prior work with enterprise customers in regulated industries (e.g., automotive, pharma, finance) and
• understanding of compliance considerations.
• Knowledge of hybrid and multi-cloud deployment patterns for AI workloads.

POSITION INFO:
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.

• 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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