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About This Role
Ecolab Digital is seeking a commercial solution focused Principal AI Architect to scale the value of AI / DS intelligence to deliver unique offerings for customers via digital software solutions with embedded AI. In this role, you will collaborate with the AI development teams, E2E solution architects, and technical engineering owners to design AI architecture within the larger solution architecture. You will own the strategic advancement of the AI tech stack and understand industry changes and opportunities to maximize solution design. You will partner with AI development teams to review adherence and navigate new solution challenges through the development process. This role is aligned with a dynamic team at the forefront of technological innovation creating cutting-edge AI for intelligent solutions that transform how our customers operate for enhanced efficiency and sustainability.
Position Details
- The desired location for this role is either Naperville, IL or St. Paul, MN with a hybrid schedule.
- Travel: Up to 10% of the time.
What you will do
- Define enterprise architecture across GenAI, agentic workflows, ML platforms, and data engineering ecosystems.
- Serve as a technical voice in leadership discussions, influencing strategy and navigating organizational challenges.
- Architect and implement scalable AI solutions including agentic ecosystems, advanced pipelines, and AI model integrations with larger end-to-end SAAS solutions.
- Evaluate and understand technologies that meet needs in support of cost optimized, multi-cloud solutions including aspects of Databricks, Snowflake, Azure and beyond.
- Elevate governance standards covering security, compliance, and responsible AI/data use.
- Mentor engineering teams, fostering autonomy, technical excellence, and architectural discipline.
- Evaluate emerging AI/data tooling and integrate reusable architectural patterns to accelerate delivery.
- Ensure alignment of architecture with business goals, scalability requirements, and innovation roadmaps.
- Provide clear, consistent communication and presentation to various stakeholders related to AI architecture strategy and implementation to drive alignment, education and impact-based decisions
- Work within an agile global resource model, planning for and delivering against initiatives
Minimum Qualifications
- Bachelors Degree in Computer Science or related field and 10 years of experience in progressive architecture and AI/DS design and engineering
- Enterprise Architecture: AI + data ecosystem design, multi-agent orchestration (LangChain, LangGraph, Haystack), enterprise-wide AI/ML standards.
- AI/ML Platforms: LLM APIs (OpenAI, Hugging Face, Anthropic, MosaicML), transformer architectures, RAG workflows, LLMOps frameworks (prompt lifecycle, monitoring).
- LLMOps frameworks (prompt lifecycle, monitoring, context engineering, harness engineering).
- Data & Cloud Infrastructure: Databricks (Delta Lake, Spark SQL, PySpark, MLflow), Snowflake (enterprise-scale warehousing), Azure (AKS, Synapse, EventHub, Logic Apps).
- Programming & Integration: Python (Transformers, LangChain, PyTorch), API integration (REST, GraphQL, gRPC), microservices/event-driven systems.
- Governance & Security: Responsible AI frameworks, privacy/compliance, OAuth2, JWT, TLS, RBAC.
- DevOps & Delivery: CI/CD (Azure DevOps, GitHub Actions), containerization (Docker, Kubernetes), observability (Prometheus, Grafana, ELK stack).
- Leadership & Strategy: Influence technical strategy, mentor engineers, simplify complex concepts for diverse stakeholders.
- Ability to thrive in an ambiguous environment, embracing change and utilizing proven reasoning in balancing practical business needs and architectural rigor
- No immigration sponsorship is available for this role at this time.
Preferred Qualifications
- Exposure to multi-cloud architectures (Azure, AWS, GCP).
- Strong understanding of token-efficient AI consumption with an ability to surface FinOps tradeoffs
- Experience with data mesh and federated AI systems.
- Familiarity with knowledge graphs and semantic search.
- Hands-on with MLOps orchestration tools (Kubeflow, MLflow, Vertex AI).
- Understanding of edge AI deployment and IoT integration.
- Experience in enterprise architecture frameworks (TOGAF, Zachman).
- Knowledge of AI ethics frameworks and regulatory compliance (EU AI Act, ISO standards).
- Demonstrated interconnected disciplinary knowledge of digital solution development
Annual or Hourly Compensation Range
The base salary range for this position is $166,300.00 - $249,600.00. This position is eligible for annual bonus pay based on performance, per plan terms. Many factors are taken into consideration when determining compensation, such as experience, education, training, geography, etc. We comply with all minimum wage and overtime laws. Benefits
Ecolab strives to provide comprehensive and market-competitive benefits to meet the needs of our associates and their families.
If you are viewing this posting on a site other than our Ecolab Career website, view our benefits at jobs.ecolab.com/working-here.
Potential Customer Requirements Notice
To meet customer requirements and comply with local or state regulations, applicants for certain customer-facing roles may need to:
- Undergo additional background screens and/or drug/alcohol testing for customer credentialing.
Americans with Disabilities Act (ADA)
Ecolab will provide reasonable accommodation (such as a qualified sign language interpreter or other personal assistance) with our application process upon request as required to comply with applicable laws. If you have a disability and require accommodation assistance in this application process, please visit the Recruiting Support link in the footer of each page of our career website.
Our Commitment to a Culture of Inclusion & Belonging
At Ecolab, we believe the best teams are inclusive. We are on a journey to create a workplace where every associate can grow and achieve their best. We are committed to fair and equal treatment of associates and applicants and recruit, hire, promote, transfer and provide opportunities for advancement based on individual qualifications and job performance. In all matters affecting employment, compensation, benefits, working conditions, and opportunities for advancement, we will not discriminate against any associate or applicant for employment because of race, religion, color, creed, national origin, citizenship status, sex, sexual orientation, gender identity and expressions, genetic information, marital status, age, disability, or status as a covered veteran.
In addition, we are committed to furthering the principles of Equal Employment Opportunity (EEO) through Affirmative Action (AA).
We will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, and the New York City Fair Chance Act.
Salary Context
This $166K-$249K range is above the median for AI Architect roles in our dataset (median: $175K across 37 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,079 AI roles we're tracking, AI Architect positions make up 1% of the market. At Ecolab, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $224,070 based on 236 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($207K) sits 7% below the category median. Disclosed range: $166K to $249K.
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.
Ecolab AI Hiring
Ecolab has 6 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer, AI Safety. Positions span Naperville, IL, US, Bengaluru East, Karnataka, India. Compensation range: $249K - $249K.
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 Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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