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About This Role
What makes us Qlik?
A Gartner® Magic Quadrant™ Leader for 15 years in a row, 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. The AI Systems Engineer Role
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. What makes this role interesting?* Frontier Technology: You’ll work directly with cutting\-edge agentic frameworks — LangGraph, LangChain, AgentCore, MCP — building systems that most engineers are only reading about.
- Real Impact: The pipelines you build will directly power customer\-facing intelligence, internal automation, and executive decision support across a 40,000\+ customer base.
- Collaborative Team: You’ll be embedded in a small, high\-velocity AI Practice team with a direct line to executive leadership and cross\-functional stakeholders across CS, Sales, and Product.
Here’s how you’ll be making an impact:* 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.
We’re looking for a teammate with:
The ideal candidate will have hands\-on experience building and shipping agentic AI systems, with deep proficiency in Python and the modern AI engineering stack:* 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. Familiarity with Qlik Cloud Analytics or Qlik Talend Cloud is a strong plus.
- 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. Familiarity with IAM, Lambda, and API Gateway a plus.
Beyond technical skills, we’re looking for someone who brings:* 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.
The location for this role is:
King of Prussia, PA
Hybrid: \#LI\-Hybrid Apply now and help change how the world transforms complex data landscapes into actionable insights and turns complex data challenges into new opportunities! More about Qlik and who we are:
Find out more about ‘Life at Qlik’ on social: Instagram, LinkedIn, YouTube, and X/Twitter, and to see all other opportunities to join us and our values, check out our Careers Page. What else do we offer?* Named in Newsweek’s ‘Americas Greatest Workplaces 2025’: https://rankings.newsweek.com/americas\-greatest\-workplaces\-2025\.
- Genuine career progression pathways and mentoring programs.
- Culture of innovation, technology, collaboration, and openness.
- Flexible, diverse, and international work environment.
Giving back is a huge part of our culture. Alongside an extra “change the world” day plus another for personal development, we also highly encourage participation in our Corporate Responsibility Employee Programs. 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. For positions in Massachusetts: It is unlawful to require or administer a lie detector test for employment. Violators are subject to criminal penalties and civil liability.
Qlik is an Equal Opportunity/Affirmative Action Employer. We are committed to fostering a workplace that is diverse, equitable and inclusive.
Qualified applicants will receive consideration for employment without regard to actual or perceived: race, color, religion, sex, sexual orientation, gender identity, pregnancy and related medical conditions, genetic information, national origin, age, marital status, protected veteran status, disability status or any other characteristic protected by applicable law. For United States applicants and employees, go to the US Department of Labor’s website to review the Equal Employment Opportunity Posters, including the “Know Your Rights” and “Pay Transparency Nondiscrimination” posters.
If you need assistance applying for a role due to a disability, please submit your request via email to [email protected]. Any information you provide will be treated according to Qlik’s Recruitment Privacy Notice. Qlik may only respond to emails related to accommodation requests.
Qlik uses artificial intelligence (e.g. Talent Intelligence developed by Eightfold) to screen and summarize resumes, assess and evaluate candidates, and score candidates for all positions at Qlik. Any hiring decision will involve a human review, and you will not be subject to decisions based solely on automated means. For questions about this tool, contact [email protected].
Qlik is not accepting unsolicited assistance from search firms for this employment opportunity. Please, no phone calls or emails. All resumes submitted by search firms to any employee at Qlik via\-email, the Internet or in any form and/or method without a valid written search agreement in place for this position will be deemed the sole property of Qlik. No fee will be paid in the event the candidate is hired by Qlik as a result of the referral or through other means.
Salary Context
This $100K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Qlik, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($112K) sits 38% below the category median. Disclosed range: $100K to $125K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Qlik AI Hiring
Qlik has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in King of Prussia, PA, US. Compensation range: $125K - $125K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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