About the position
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.
Minimum Requirements:
Qualifications/Experience:
Appropriate academic qualitfication 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.
Essential Skills Requirements:
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.
Advantageous Skills Requirements:
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.
Desired Skills:
- LangChain
- PyTorch
- TensorFlow
