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About This Role
Description:
AI Snowflake Engineer
We are HALO! We connect people and brands to create unforgettable, meaningful, and lasting experiences that build brand engagement and loyalty for our over 60,000 clients globally, including over 100 of the Fortune 500. Our nearly 2,000 employees and 1,000 Account Executives located in 40+ sales offices across the United States are the reason HALO is #1 in our $25B industry.
HALO Branded Solutions is seeking a skilled and curious Snowflake AI Engineer to join our growing Data & Analytics team. In this role, you will be at the forefront of how we unlock the value of our enterprise data — building a robust Semantic Layer that brings structure and business context to our data assets, and developing intelligent Cortex Agents that empower our teams to get instant, trusted answers through natural language conversations. You will work closely with data engineers, business analysts, and stakeholders across the organization to understand how our data is created and used, translate that understanding into a well-governed semantic model, and bring it to life through Snowflake's AI-native capabilities. This is a high-impact, highly visible position for someone who loves both the craft of data engineering and the challenge of making data accessible to everyone.
Responsibilities
Semantic Layer Development
- Partner with stakeholders and data owners across HALO to develop a deep understanding of our enterprise data domains — including merchandise, orders, programs, client accounts, and recognition data.
- Design and implement a comprehensive Semantic Layer within Snowflake, applying consistent business definitions, metrics, hierarchies, and context to our data assets.
- Define and maintain semantic models, logical data objects, and certified metrics that serve as the trusted foundation for analytics and AI use cases.
- Collaborate with the broader data team to ensure the Semantic Layer stays aligned with evolving data models, source systems, and business logic.
- Document semantic definitions, lineage, and business rules to support data literacy and governance across the organization.
Cortex Agent Development
- Design, build, and deploy Snowflake Cortex Agents that allow business users to interact with enterprise data through natural language queries.
- Develop and fine-tune agent logic, intent parsing, and response generation to ensure accurate, reliable, and business-context-aware answers.
- Integrate Cortex Agents with the Semantic Layer to ground AI responses in governed, certified data.
- Establish feedback loops and monitoring to continuously improve agent performance, answer quality, and user trust.
- Work with end users and stakeholders to identify high-value agent use cases, iterate on functionality, and drive adoption.
Platform & Collaboration
- Stay current with Snowflake's evolving AI and data cloud capabilities, evaluating and championing new features relevant to our roadmap.
- Contribute to data engineering best practices, coding standards, and peer review within the team.
- Communicate technical concepts clearly to non-technical audiences, enabling business partners to understand and trust the data and AI tools you build.
- Support data governance initiatives by promoting consistent, well-documented, and auditable data practices.
Requirements:
- 3+ years of experience working with Snowflake in a data engineering, analytics engineering, or AI/ML engineering capacity.
- Demonstrated experience designing and implementing semantic or logical data models (e.g., Snowflake Semantic Layer, dbt Semantic Layer, LookML, or equivalent).
- Hands-on experience with Snowflake Cortex features, including Cortex Analyst, Cortex Search, or AI/ML functions.
- Strong SQL proficiency with deep knowledge of Snowflake-specific features (dynamic tables, materialized views, streams, tasks, row access policies).
- Solid understanding of enterprise data concepts — dimensional modeling, data vault, or medallion architecture patterns.
- Experience working in cross-functional environments, translating business requirements into technical data solutions.
- Strong communication skills — able to articulate data concepts to both technical and non-technical stakeholders.
Preferred Experience
- Experience with LLM integration patterns, prompt engineering, or AI agent frameworks.
- Familiarity with dbt for data transformation and semantic modeling.
- Working knowledge of Python, particularly for Snowpark or Cortex API integrations.
- Background in branded merchandise, retail, supply chain, or B2B services data environments.
- Snowflake SnowPro certification (Core or Advanced: Data Engineer).
- Experience with data observability and quality tooling (e.g., Monte Carlo, Elementary, or Snowflake data quality features).
Compensation: The estimated base salary range for this position is between $110,000-$140,000 annually. Please note that this pay range serves as a general guideline and reflects a broad spectrum of labor markets across the US. While it is uncommon for candidates to be hired at or near the top of the range, compensation decisions are influenced by various factors. At HALO, these include, but are not limited to, the scope and responsibilities of the role, the candidate's work experience, location, education and training, key skills, internal equity, external market data, and broader market and business considerations.
Benefits: At HALO, we offer benefits that support all aspects of your life, helping you find a work-life balance that’s right for you. Our comprehensive benefits include nationwide coverage for Medical, Dental, Vision, Life, and Disability insurance, along with additional Voluntary Benefits. Prepare for your financial future with our 401(k) Retirement Savings Plan, Health Savings Accounts (HSA), and Flexible Spending Accounts (FSA).
Application Information: To apply to this opportunity, click the APPLY button at the top right or very bottom of the screen to complete our online application. A resume is optional, so you may choose to upload and have the application prefill with your information. There are 5 sections to complete in total, including General information, Work History, Education, Compliance, and optional demographic questions. Once you have successfully submitted your application, you will receive a submission confirmation email from our system.
Application Deadline: Applications are reviewed and processed only when there is a specific need or opportunity, rather than on a fixed schedule or at a set deadline. Because they are reviewed on an as-needed basis, a job posting will be removed once the position has been filled or is no longer available.
More About HALO:
At HALO, we energize our clients' brands and amplify their stories to capture the attention of those who matter most. That’s why over 60,000 small- and mid-sized businesses partner with us, making us the global leader in the branded merchandise industry.
- Career Advancement: At HALO, we’re passionate about promoting from within. Internal promotions have been key to our exponential growth over the past few years. With so many industry leaders at HALO, you’ll have the opportunity to accelerate your career by learning from their experience, insights, and skills. Plus, you'll gain access to HALO’s influential global network, leadership opportunities, and diverse perspectives.
- Culture: We love working here, and we’re confident you will too. At HALO, you’ll experience a culture of ingenuity, inclusion, and relentless determination. We push the limits of possibility and imagination by staying curious, humble, and bold breaking through yesterday’s limits. Diversity fuels our creativity, and we thrive when each of us contributes to an inclusive environment based on respect, dignity, and equity. We hold ourselves to a high standard of excellence with a commitment to results and supporting one another with accountability, transparency, and dependability.
- Recognition: At HALO, your success is our success. You can count on us to celebrate your wins. Colleagues across the company will join in recognizing your milestones and nominating you for awards. Over time, you’ll accumulate recognition that can be converted into gift cards, trips, concert tickets, and merchandise from your favorite brands.
- Flexibility: Many of our roles offer hybrid work options, and we pride ourselves on flexible schedules that help you balance professional and personal demands. We believe that supporting our customers is a top priority and trust that you and your manager will collaborate to create a schedule that achieves this goal.
HALO is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We insist on an environment of mutual respect where equal employment opportunities are available to all applicants without regard to race, color, religion, sex, pregnancy (including childbirth, lactation and related medical conditions), national origin, age, physical and mental disability, marital status, sexual orientation, gender identity, gender expression, genetic information (including characteristics and testing), military and veteran status, and any other characteristic protected by applicable law. Inclusion is a core value at HALO and we seek to recruit, develop and retain the most talented people.
HALO participates in E-Verify. Please see the following notices in English and Spanish for important information: E-Verify Participation and Right to Work.
HALO is committed to working with and providing reasonable accommodations to individuals with disabilities. If you need reasonable accommodation because of a disability for any part of the employment process – including the online application and/or overall selection process – you may email us at [email protected]. Please do not use this as an alternative method for general inquiries or status on applications as you will not receive a response. Reasonable requests will be reviewed and responded to on a case-by-case basis.
Salary Context
This $110K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At HALO, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($125K) sits 41% below the category median. Disclosed range: $110K to $140K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
HALO AI Hiring
HALO has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in IL, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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