AI Systems Engineer

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.

• 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.

What Makes This Role Interesting

• 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.

Requirements

• 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.

What We Offer

• 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.
• Comprehensive Benefits: 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.

About Qlik

Qlik transforms complex data landscapes into actionable insights, driving strategic business outcomes. Serving over 40,000 global customers, our portfolio leverages pervasive data quality and advanced AI/ML capabilities that lead to better decisions, faster. We excel in integration and governance solutions that work with diverse data sources, and our real-time analytics uncover hidden patterns, empowering teams to address complex challenges and seize new opportunities.

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