Interested in this AI/ML Engineer role at Ecolab?
Apply Now →Skills & Technologies
About This Role
As the AI Solutions Architect – Legal, you will lead the architecture, design, and delivery of enterprise AI solutions that transform how Legal, Compliance, Procurement, and Regulatory stakeholders operate. You will be responsible for translating complex legal business challenges into scalable, secure, governed, and production ready AI solutions that integrate seamlessly with Contract Lifecycle Management (CLM) platforms, legal workflow tools, enterprise systems, and authoritative systems of record.
This is a hands on architecture role, not a purely advisory position. In addition to owning solution architecture and technical direction, you will actively design, develop, validate, and deploy production ready AI capabilities, reference implementations, integrations, and workflow automation solutions.
You will establish architecture patterns, guide platform and technology decisions, evaluate buy versus build opportunities, and ensure AI is embedded into governed and auditable legal processes. Success in this role requires deep technical expertise, strong solution architecture capabilities, and the ability to influence stakeholders across Legal, IT, Security, Compliance, Procurement, and enterprise technology teams.
What you will do
Solution Architecture \& Technical Leadership
- Architect end\-to\-end AI enabled legal solutions spanning intake, contract lifecycle management, compliance workflows, legal operations, document intelligence, and post signature insights.
- Translate business challenges into scalable technical architectures, including application design, workflow orchestration, integration patterns, data flows, security controls, AI services, and deployment models.
- Define solution blueprints, reference architectures, reusable design patterns, and technical standards for Legal AI initiatives.
- Lead architecture reviews and technical decision making across legal technology platforms, enterprise systems, and AI capabilities.
- Drive technology strategy and roadmap recommendations for legal workflow automation and AI enabled legal operations.
AI Solution Design \& Delivery
- Design and deliver production ready AI agents, copilots, orchestration services, APIs, and automation solutions that solve legal business challenges.
- Develop AI enabled capabilities supporting contract analysis, clause extraction, obligation tracking, document summarization, compliance validation, legal intake, risk identification, and workflow acceleration.
- Design and implement agentic and multi agent architectures that integrate intelligence services into governed legal workflows.
- Establish evaluation frameworks, monitoring patterns, observability standards, and performance metrics to ensure reliable AI solution operation.
- Lead architecture decisions regarding model selection, orchestration approaches, retrieval strategies, and enterprise AI integration patterns.
Enterprise Integration \& Platform Architecture
- Design secure, scalable integrations between AI capabilities, CLM platforms, workflow tools, enterprise applications, and systems of record.
- Define integration patterns that maintain proper separation between:
+ AI reasoning and intelligence layers
+ Workflow orchestration platforms
+ Business applications
+ Data services
+ Systems of record
- Ensure legal systems retain authoritative ownership of records, approvals, contracts, and compliance workflows while AI capabilities augment decision making and productivity.
- Collaborate with enterprise architecture, platform engineering, and security teams to establish scalable solution patterns.
Buy vs. Build \& Vendor Evaluation
- Lead buy versus build assessments for Legal AI capabilities and workflow solutions.
- Evaluate vendors, platforms, and emerging technologies against business requirements, integration complexity, risk, security, scalability, and total cost of ownership considerations.
- Provide technical due diligence, architecture recommendations, implementation estimates, and adoption strategies for legal technology investments.
- Partner with vendors and implementation teams to ensure solutions align with enterprise architecture standards.
Governance, Security \& Responsible AI
- Establish architecture standards supporting Responsible AI, security, privacy, regulatory compliance, auditability, and enterprise governance requirements.
- Design controls and guardrails that ensure AI solutions operate safely within regulated legal and compliance processes.
- Partner with Security, Risk, Compliance, and Legal stakeholders to incorporate governance requirements into solution designs.
- Ensure solutions meet enterprise standards for data protection, access control, monitoring, resiliency, and operational support.
Technical Leadership \& Team Enablement
- Provide architecture leadership, technical mentorship, and implementation guidance to engineers, data scientists, and solution teams.
- Lead technical design reviews and establish engineering best practices for AI development and deployment.
- Drive adoption of reusable components, reference implementations, and enterprise solution standards.
- Influence cross functional teams through technical expertise, strategic thinking, and collaborative leadership.
- Serve as the primary technical authority for Legal AI architecture and solution design.
Minimum Qualifications
- Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical field.
- 8 years of software engineering experience with the ability to personally design, develop, test, and deploy production ready solutions.
- 5 years of experience architecting and delivering enterprise software, workflow, integration, or AI enabled solutions in production environments.
- 5 years’ experience serving as a Solution Architect, Lead Engineer, Technical Architect, or comparable technical leadership role.
- Experience building AI enabled applications leveraging large language models, agentic architectures, retrieval systems, orchestration frameworks, and enterprise AI services.
- Demonstrated experience architecting enterprise workflows that span multiple business systems, applications, data sources, and organizational stakeholders.
- 5 years’ experience with Python, API design, integration patterns, cloud native development, and modern software architecture principles.
- Experience leveraging AI assisted development tools (e.g., Claude Code, GitHub Copilot, Cursor, or similar) to accelerate software delivery and solution development.
- Proven ability to make architecture tradeoff decisions balancing business value, technical complexity, security, scalability, maintainability, and total cost of ownership.
- Strong communication and facilitation skills with the ability to engage technical teams, business stakeholders, legal professionals, and executive leadership.
- No immigration sponsorship is available for this role at this time.
Preferred Qualifications
- Experience implementing or architecting solutions for Contract Lifecycle Management (CLM), legal operations, compliance, procurement, regulatory affairs, or enterprise workflow automation.
- Familiarity with platforms such as Agiloft, Eudia, ServiceNow, GEP, Harvey AI or similar legal technology ecosystems.
- Experience designing AI solutions for:
+ Contract review and analysis
+ Clause and obligation extraction
+ Legal research support
+ Compliance validation
+ Legal intake and matter routing
+ Marketing compliance workflows
+ Procurement contracting
- Knowledge of enterprise architecture frameworks, integration platforms, API management, and workflow orchestration platforms.
- Experience implementing Responsible AI, AI governance, model evaluation, and enterprise AI monitoring practices.
- Familiarity with security, privacy, auditability, and risk management requirements associated with regulated business processes.
- Experience influencing platform, vendor, and architecture decisions at enterprise scale.
Annual or Hourly Compensation Range
The base salary range for this position is $153,900\.00 \- $230,800\.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 $153K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Ecolab, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($192K) sits 12% below the category median. Disclosed range: $153K to $230K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Ecolab AI Hiring
Ecolab has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Saint Paul, MN, US. Compensation range: $230K - $230K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.