AI Systems Engineer

Qlik•Boston, MA

4d•$100,000 - $125,000•Hybrid

About The Position

As an AI Systems Engineer on Qlik’s AI Practice team, you will design and build 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 work at the frontier of multi-agent orchestration — connecting large language models to Qlik’s data ecosystem and beyond, turning complex data signals into reliable, automated action. This role is located in King of Prussia, PA and is a hybrid position.

Requirements

Python Expertise: Python is your primary language. You write clean, production-grade code, build robust data pipelines, and are comfortable owning the full lifecycle from prototype to deployment.
Agentic Frameworks: Practical experience with LangGraph, LangChain, AgentCore, or comparable multi-agent orchestration frameworks — including agent routing, state management, tool use, memory, and evaluation.
LLM Integration: Demonstrated experience integrating large language models via AWS Bedrock, Anthropic APIs, or similar — including prompt engineering, context management, and output validation.
Data Source Integrations: Hands-on experience connecting applications to modern data platforms including Snowflake, Apache Iceberg, OpenSearch, or similar.
MCP / Tool-Use Patterns: Familiarity with Model Context Protocol (MCP) or equivalent patterns for giving agents structured, governed access to external data systems.
API Development: Experience building production REST APIs using FastAPI or comparable Python frameworks.
Cloud Platforms: Hands-on AWS experience, with Bedrock exposure strongly preferred.
Bias for Action: You ship, instrument, and iterate — you don’t wait for perfect requirements.
Strong Communication: Ability to translate complex agentic system behavior to non-technical stakeholders clearly and concisely.
Security and Governance Mindset: Awareness of responsible AI practices, data privacy considerations, and the importance of auditability in agentic systems.
Collaborative Spirit: Comfortable working across functions and levels, from CSMs to the C-suite.

Nice To Haves

Familiarity with Qlik Cloud Analytics or Qlik Talend Cloud is a strong plus.
Familiarity with IAM, Lambda, and API Gateway a plus.

Responsibilities

Build Multi-Agent Pipelines: Design and implement orchestrated multi-agent systems using LangGraph and AgentCore, including routing logic, evaluation loops, retry mechanisms, and agent specialization patterns.
Develop with LangChain: Leverage LangChain to build sophisticated prompt pipelines, tool-augmented agents, memory constructs, and retrieval-augmented generation workflows.
Integrate Across the Data Ecosystem: Build reliable, performant Python integrations connecting agents to Qlik Cloud Analytics, Qlik Talend Cloud, Snowflake, Apache Iceberg, OpenSearch, and other data sources via MCP and direct API patterns.
Connect via MCP: Develop and maintain Python-based integrations with Qlik’s MCP server to give agents real-time, structured access to Qlik platform data and telemetry.
Deploy on AWS Bedrock: Leverage AWS Bedrock to host, invoke, and manage LLM-powered agents at scale, ensuring reliability, cost efficiency, and security compliance.
Build FastAPI Services: Develop lightweight, production-ready API services that expose agentic capabilities to downstream consumers and orchestration platforms.
Iterate Rapidly: Operate in a fast-moving incubator environment — prototype quickly, instrument your work, and evolve solutions based on real usage signals.
Collaborate Cross-Functionally: Partner closely with Customer Success, Sales, and Analytics stakeholders to translate business requirements into agentic architectures.