The AI Systems Engineer Role

As an AI Systems Engineer on Qlik's AI Practice team, you will own the infrastructure, deployment, and reliability of production-grade agentic AI systems that power the next generation of intelligent automation at Qlik. Reporting to the Global Head of AI Strategy, Policy, and Governance, you will operate at the intersection of cloud infrastructure engineering and AI systems, provisioning, securing, and scaling the AWS environments that make our AI pipelines run reliably and cost-efficiently.

What makes this role interesting?

• Ownership: You'll own real infrastructure. This is not a role where you configure demos, you will provision, harden, and operate the AWS environments that run Qlik's AI agents at scale.
• Frontier Stack: Work directly with AWS Bedrock AgentCore, a capability that most cloud engineers are only beginning to encounter. You'll be among the first teams operationalizing it in production.
• High Impact: Small team, direct line to executive leadership, real decisions. You'll shape how Qlik's AI infrastructure evolves from the ground up.

Here's how you'll be making an impact:

• IaC and Cloud Provisioning: Design and maintain infrastructure-as-code (Terraform) for all AI workloads: Bedrock agents, AgentCore runtimes, Lambda functions, API Gateway endpoints, and supporting data services.
• AWS Bedrock AgentCore: Own deployment, configuration, and scaling of AWS Bedrock AgentCore environments, including model invocation routing, session management, and memory backends.
• Security and IAM: Architect secure, least-privilege IAM policies for agent runtimes, MCP integrations, cross-account access patterns, and service-to-service authentication.
• CI/CD Pipelines: Build and maintain CI/CD pipelines (GitHub Actions, AWS CodePipeline) for agentic workloads, covering infrastructure changes, Lambda deployments, and agent configuration updates.
• Observability and Cost Management: Instrument AI systems with OpenTelemetry, CloudWatch, and X-Ray. Own observability strategy: traces, metrics, cost dashboards, and alerting across agent pipelines.
• Networking: Manage networking topology for AI workloads: VPC design, PrivateLink, security groups, and egress controls to ensure data never leaves governed boundaries unexpectedly.
• Data Infrastructure: Build and operate the data infrastructure supporting agents: OpenSearch for semantic search and vector retrieval, and S3 lifecycle policies for knowledge artifact storage.
• Collaboration: Collaborate with AI engineers on MCP server deployments, containerized agent runtimes (ECS/Fargate), and performance tuning to hit latency and cost targets.

We're looking for a teammate with:

The ideal candidate will have hands-on experience owning production infrastructure, with deep proficiency across AWS and the modern cloud engineering stack:

• Infrastructure as Code: You have strong hands-on Terraform experience. You version-control infrastructure the same way you version-control code.
• AWS Platform Depth: Deep AWS experience across Bedrock (model invocation, agents), Lambda, API Gateway, ECS/Fargate, App Runner, IAM, VPC, S3, CloudWatch, and X-Ray. Bedrock AgentCore exposure is a strong plus.
• Security Mindset: You understand the AWS IAM model deeply: roles, policies, SCPs, permission boundaries, and cross-account trust. You instinctively scope to least privilege.
• CI/CD: You build pipelines that deploy infrastructure and application code reliably. GitHub Actions, CodePipeline, or equivalent. You know how to roll back safely.
• Observability: Experience with OpenTelemetry, CloudWatch Logs Insights, and distributed tracing. You care about visibility into what AI systems are actually doing and what they cost.
• Python: Comfortable with Python for scripting, Lambda functions, and lightweight automation. You don't need to be an ML engineer, but you can read and modify agent code.
• Containers: Comfortable with containerized workloads (Docker, ECS, Fargate, App Runner). Experience running long-running agent processes or streaming inference endpoints is a plus.
• Data Infrastructure: You understand vector search infrastructure and are comfortable operating OpenSearch clusters for semantic retrieval. Experience with RAG pipeline data stores preferred.

Beyond technical skills, we're looking for someone who brings:

• Ownership: You close the loop. You don't just provision infrastructure, you monitor it, cost-optimize it, and improve it without being asked.
• Comfort with Ambiguity: AI infrastructure is still being invented. You are energized by ambiguity and comfortable writing the runbook that didn't exist before you joined.
• Communication: Able to explain an IAM boundary decision or a rate-limiting architecture to an AI engineer or a business stakeholder equally well.
• Security and Governance Mindset: You think about what happens when things fail, when costs spike, when an agent calls an endpoint it shouldn't. You build guardrails proactively.

The location for this role is:

King of Prussia, PA

Boston, MA

Hybrid: #LI-Hybrid

Salary and Benefits: The anticipated base salary range for this role is $100,000 to $125,000 USD. Final compensation offered by Qlik will be based on factors such as the candidate's location, job-related skills, education, experience, and other business and organizational needs.

This position is eligible for comprehensive benefits, including - but not limited to - medical, dental, and vision coverage life and AD&D, short and long-term disability coverage, paid time off, paid parental / maternity leave, participation in a 401(k) program that includes company match, and many other additional voluntary benefits.

Application Window: The application window is 60 days, but applicants are encouraged to apply as soon as possible. The posting will be removed before the application window closes if the position is filled.