Staff Software Development Engineer – Enterprise AI Infrastructure

GRAIL

Senior role leading the design and development of an AI platform for early cancer detection at GRAIL. Focusing on AWS and Kubernetes infrastructure with cross-functional collaboration.

Posted 7/22/2026full-timeMenlo Park • California, North Carolina • 🇺🇸 United StatesLead💰 $169,000 - $224,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in AWS Cloud Architecture, Container Orchestration, and Agentic AI Development, with a strong focus on secure integrations and compliance within regulated environments. Proven ability to lead technical teams, mentor engineers, and drive strategic AI platform initiatives.

Highest-signal resume keywords

AWS Cloud ArchitectureContainer OrchestrationAgentic AI DevelopmentIdentity And Access ManagementRegulatory Compliance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Amazon EKSKubernetesPythonTypeScriptGoTerraformAWS CDKModel Context ProtocolDeterministic Policy EnforcementCI/CD Pipelines

Soft Skills

Problem-SolvingLeadershipCommunicationMentoringStrategic Thinking

Tools & Technologies

OpenSearchOktaAuth0GenAI Observability ToolsHelmPrivateLinkJWTLangChainLangGraphClaude Agent SDK

Certifications & Qualifications

Bachelor's DegreeMaster's DegreePhD

Industry Keywords

Cybersecurity PrinciplesISO 27001NISTSOC 2HIPAAIVDDIVDRFDA 21 CFRAI GovernanceData Integrity

Tech Stack

Tools & technologies

AWSCloudCyber SecurityGoKubernetesPythonTerraformTypeScript

About the role

Key responsibilities & impact

Lead the end-to-end design, development, deployment, and monitoring of a scalable, governed enterprise AI platform leveraging Amazon EKS and AWS native services (e.g., Bedrock, OpenSearch Serverless, KMS, VPC).
Design and implement agentic AI workflows, specialized autonomous agents, and multi-agent systems using advanced LLM orchestration techniques and agent frameworks.
Architect and manage secure integrations using the Model Context Protocol (MCP) to connect the AI platform with internal systems, vector databases, and third-party SaaS applications (e.g., Google Workspace, Slack).
Build and enforce strict identity, authorization, and zero-trust token brokering flows leveraging Okta, Auth0, and custom JWT authorizers to ensure secure, least-privilege tool execution.
Implement deterministic policy controls (e.g., Cedar policy engine) to enforce role-based access, approval gates, and human-in-the-loop checks at the API gateway level.
Develop and maintain highly isolated, scalable containerized runtime environments (e.g., Kubernetes pods on Amazon EKS) for secure AI model execution, tool usage, and knowledge retrieval.
Establish and maintain comprehensive audit trails and observability for all AI interactions, utilizing AWS CloudTrail and GenAI observability tools (e.g., OpenTelemetry) to track cost, latency, and tool calls.
Collaborate with Product Management, Security, Regulatory, and business stakeholders to translate enterprise requirements into scalable, compliant AI infrastructure solutions.
Troubleshoot and resolve complex technical issues involving cloud infrastructure, Kubernetes networking, network isolation (PrivateLink), and agentic workflows.
Contribute to technology roadmaps, AI infrastructure strategy, and long-term platform evolution initiatives.
Mentor engineers, software developers, and technical teams while promoting engineering excellence, infrastructure-as-code (IaC) best practices, and continuous improvement.
Partner with Quality, Regulatory, Privacy, Security, and Compliance functions to ensure software and AI systems operate in accordance with applicable regulatory requirements and company policies.

Requirements

What you’ll need

Bachelor's degree or equivalent in Computer Science, Software Engineering, Artificial Intelligence, Cloud Computing, or related field; Master's or PhD preferred.
8-12 years of relevant software development and cloud infrastructure experience with demonstrated technical leadership.
Deep expertise in AWS cloud architecture and container orchestration, specifically with Amazon EKS, Kubernetes networking, network isolation (VPC, PrivateLink), IAM, KMS, and GenAI services (e.g., AWS Bedrock).
Proven experience in agentic AI development, building autonomous agents, and orchestrating LLM tool-calling workflows using frameworks like LangChain, LangGraph, AutoGen, or Claude Agent SDK.
Hands-on experience implementing the Model Context Protocol (MCP) or building robust, governed API/tool integrations for LLMs.
Strong background in identity and access management (IAM), OAuth, JWT, and integrating with enterprise IdPs (Okta, Auth0) for scoped, token-based authorization.
Advanced proficiency in programming languages such as Python, TypeScript, or Go, and infrastructure-as-code tools (Terraform, AWS CDK).
Experience with vector databases, RAG (Retrieval-Augmented Generation) architectures, and row-level access controls (e.g., OpenSearch, FAISS, pgvector).
Proficiency with CI/CD pipelines, MLOps practices, Kubernetes ecosystem tools (e.g., Helm), containerization, and modern observability stacks.
Demonstrated level of knowledge regarding applicable regulatory standards commensurate with the position's complexity and scope, contributing to organizational regulatory compliance. Minimal applicable standards for this position include:
Cybersecurity principles, tools, and control frameworks (e.g., ISO 27001, NIST, SOC 2, HIPAA)
Operations within the regulated medical device environment (e.g., IVDD, IVDR, FDA 21 CFR 800 series, FDA 21 CFR Part 11)
AI governance, software validation, data integrity, and risk management principles applicable to regulated environments
Deep expertise in cloud infrastructure, containerized environments, agentic artificial intelligence, and secure distributed system design.
Exceptional problem-solving and analytical skills with the ability to address ambiguous, high-impact technical challenges in AI orchestration and Kubernetes scaling.
Strong leadership and influence skills, capable of driving alignment across engineering, security, regulatory, and business stakeholders.
Excellent communication skills with the ability to explain complex LLM behaviors, infrastructure architectures, and security boundaries to technical and non-technical audiences.
Proven mentoring and coaching capabilities that elevate cloud engineering and AI talent.
Strong understanding of AI safety, prompt injection defenses, secure tool execution, and deterministic policy enforcement.
Strategic thinking with the ability to balance long-term enterprise AI platform vision with near-term business delivery.
High adaptability and intellectual curiosity regarding emerging agentic AI frameworks, MCP specifications, and cloud computing trends.

Benefits

Comp & perks

flexible time-off or vacation
a 401(k) retirement plan with employer match
medical, dental, and vision coverage
carefully selected mindfulness programs