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About This Role
Let’s be unstoppable together!
Circana is a leading provider of technology, AI, and data solutions for consumer packaged goods companies, manufacturers, and retailers. Our predictive analytics and Liquid Data® platform help clients measure market share, uncover consumer behavior, and drive growth—powered by six decades of expertise and an expansive, high-quality data set.
At Circana, we are fueled by our passion for continuous learning and growth, we seek and share feedback freely, and we celebrate victories both big and small in an environment that is flexible and accommodating to our work and personal lives. We’re a global company dedicated to fostering inclusivity and belonging. We value and celebrate the unique experiences, cultures, and viewpoints that each individual brings. By embracing a wide range of backgrounds, skills, expertise, and beyond, we create a stronger, more innovative environment for our employees, clients, and communities. With us, you can always bring your full self to work. Join our inclusive, committed team to be a challenger, own outcomes, and stay curious together. Circana is proud to be Certified™ by Great Place To Work®. This prestigious award is based entirely on what current employees say about their experience working at Circana. Learn more at www.circana.com. (https://www.circana.com.)
Role Overview
Circana is building an agentic ecosystem on the industry's richest view of consumer behavior: intelligent systems that reason, plan, and act across the data the world's largest brands and retailers run on. AI\Agentic Architects lead that work solution by solution. Embedded with the experts who hold the domain knowledge, they design the agents, set the patterns, and own the outcome from first conversation to production use. Each architect anchors a pod of engineers and carries the technical direction for a solution family for the length of a build.
Job Responsibilities
- Lead the design and delivery of agentic systems for a solution family, from domain discovery through production use.
- Translate domain expertise into intent ontologies, tool contracts, and evaluation sets, working directly with subject matter experts.
- Own harness engineering: context management, tool contracts and MCP servers, guardrails, retries and graceful degradation, tracing and replay.
- Establish evaluation as infrastructure: golden traces, regression suites, routing tests, offline and online measurement.
- Set reusable patterns (shared services, tools, and conventions) that make every subsequent build faster than the last.
- Anchor a pod of engineers: set technical direction, review the work, and raise the bar.
- Represent the work credibly to product owners, architects, security, executives, and client stakeholders.
Requirements
- A strong track record of designing and shipping agentic systems into production for real users.
- Deep production fluency with agent orchestration frameworks: LangGraph, Google ADK, or Microsoft Agent Framework.
- Production experience across multiple model families, with clear judgment on when to reach for which.
- Production experience on at least one major cloud AI stack.
- Mastery of the practical craft: context management, prompt design, plan generation, structured outputs, and tool design.
- Daily fluency with agentic coding tools; Claude Code preferred.
- Command of the agentic development lifecycle: specs as source of truth, evals as continuous integration, prompt and tool versioning, and trace-driven debugging.
- Communication that lands at every altitude: engineers, domain experts, and senior executives alike.
Preferred
- Consumer goods, retail, or large-scale measurement data background.
- Prior forward deployed or field engineering experience at an AI or data company.
- Public artifacts: talks, open source, technical writing.
Tech Stack & Tools
- Orchestration: LangGraph, Google ADK, Microsoft Agent Framework
- Models: Anthropic, Google, OpenAI, open-weight families
- Cloud: GCP Vertex AI, Azure AI Foundry
- Protocols: MCP, A2A, AG-UI, A2UI
- Engineering: Python, TypeScript, Claude Code
- Delivery: evaluation harnesses as CI, tracing and observability tooling
Circana Behaviors
Beyond technical skills, experience, and role-specific attributes, these shared behaviors are fundamental to our culture and success. We seek individuals who consistently demonstrate and champion these behaviors in their daily work:
- Stay Curious: Being hungry to learn and grow, always asking the big questions.
- Seek Clarity: Embracing complexity to create clarity and inspire action.
- Own the Outcome: Being accountable for decisions and taking ownership of our choices.
- Center on the Client: Relentlessly adding value for our customers.
- Be a Challenger: Never complacent, always striving for continuous improvement.
- Champion Inclusivity: Fostering trust in relationships engaging with empathy, respect, and integrity.
- Commit to each other: Contributing to making Circana a great place to work for everyone.
Location: This position can be located in the following area(s): Chicago, IL (Hybrid) or NYC, NY (Hybrid)
The below range reflects the range of possible compensation for this role at the time of this posting. We may ultimately pay more or less than the posted range. This range may be modified in the future. An employee’s position within the salary range will be based on several factors including, but not limited to, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, shift, travel requirements, sales or revenue-based metrics, any collective bargaining agreements, and business or organizational needs. The salary range for this role is $200,000 USD to $250,000 USD.
This job is also eligible for bonus pay.
We offer a comprehensive package of benefits including paid time off, medical/dental/vision insurance and 401(k) to eligible employees.
An offer of employment may be conditional upon successful completion of a background check in accordance with local legislation and our candidate privacy notice (https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.circana.com%2Fcandidate-privacy-notice%2F&data=05%7C02%7CJasmine.Taylor%40circana.com%7C3108d7a5a34247751fc408dc870da8b8%7C43728c2044474b27ac2e4bdabb3c0121%7C0%7C0%7C638533739951470893%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=KS4AzsLAW3VAvnXaDGxSBtlAVGuZ%2B6kqfrfh55uF3wc%3D&reserved=0). Your current employer will not be contacted without your permission.
You can apply for this role through the Circana careers website or Intranet site for internal candidates. This role is subject to AI-assisted screening. Circana uses artificial intelligence (AI) to assess resumes for alignment with job requirements by helping locate details in resumes that relate to the job description.
This position is expected to remain open for approximately 30 days and may close earlier if sufficient qualified candidates are identified.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $200K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Circana, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($225K) sits 6% above the category median. Disclosed range: $200K to $250K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Circana AI Hiring
Circana has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Bengaluru, Karnataka, India, Chicago, IL, US, US. Compensation range: $200K - $250K.
Location Context
AI roles in Chicago pay a median of $194,700 across 390 tracked positions. That's 9% below the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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