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About This Role
Challenge Yourself and Impact the Future!
Element Solutions Inc (NYSE:ESI) is a leading specialty chemicals company whose operating businesses formulate a broad range of solutions that enhance the performance of products people use every day. Developed in multi-step technological processes, our innovative solutions enable our customer manufacturing processes in several key segments, including electronic circuitry, communication infrastructure, automotive systems, industrial surface finishing, and offshore energy.
Customers of our businesses use our innovation as a competitive advantage, relying on us to help them navigate in fast-paced, high-growth markets. For example, in-care technology, from infotainment to driver assistance, is accelerating the paste of new product development and automotive markets, and with a deep market expertise in electronics, we sit at the intersection of the fast-growing market, changing the competitive playing field for automotive manufacturers, with a long-standing presence.
We strive to embody the five 'Elements of our Culture'- our '5C's'; Challenge, Commit, Collaborate, Choose, and Care. These core values are the foundation of our organization which our employees embrace in their interactions with customers, colleagues and other stakeholders, to drive financial performance and create a rewarding work environment.
Who are we looking for?
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Element Solutions Inc is searching for a strategic, business-savvy, technically literate leader, to drive and own end-to-end lifecycle of Data, AI, Automation and analytics products within the enterprise. As key leader within ESI’s growing Data and AI office, this role will act as the bridge between business stakeholders, technical teams, and executive leadership – translating business needs into technical requirements, defining and prioritizing use cases, overseeing delivery, and maximizing value from investments. This role will manage a team of project managers and analysts and perform additional management and administrative functions within the department.
The ideal candidate will be a true business partner, with strong technical and interpersonal and skills, and must develop strong relationships while managing expectations. This is an exciting opportunity for someone who wants to play a key role in developing and scaling the organization’s AI and Automation capabilities.
What will you be doing?
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- Own execution of the organization’s Data, AI, Automation, and Analytics portfolio – develop and manage the Product Management Office
- Collaborate with business stakeholders across functions to discover business needs and surface high-value AI and Automation use cases
- Translate business problems and needs into clear product definition, functional requirements, success criteria, value drivers, KPIs
- Manage full product lifecycle: value hypothesis, prioritization, experimentation, build, deployment, scale and adoption
- Work closely with data scientists, AI and data engineers, and other technical teams to scope and prioritize initiatives
- Drive adoption across all levels of the organization, ensuring usage of and value created by AI technology, and adherence to change in business processes
- Develop and deliver change management mechanisms to ensure adoption and scaling of AI and Automation solutions
- Develop value frameworks measuring effectiveness of the portfolio
Who are You?
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- 10+ years in product management, consulting, digital/AI solution delivery
- 5+ years designing and leading AI and automation product lifecyles
- Comprehensive knowledge in areas of artificial intelligence, machine learning, automation
- BPMN/business process modeling and redesign
- Strong business and technical acumen with ability to translate business needs into technical requirements
- Understanding of agentic automation, RAG, prompt/pattern design, vector database fundamentals, HITL design
- Strong understanding of various cloud-based data platforms such as Azure, Snowflake, AWS, etc
- Proven ability to lead and manage all phases of a project lifecycle (SDLC, Agile). Hands-on experience with Jira, MS Project required
- Knowledge of PMO, CMM and Six Sigma methodologies and standards
- Ability to communicate at all levels within the organization, providing the appropriate level of detail on the right information, in an international, multi-cultural work environment
- Chemical Industry experience preferred
- Strong oral, written communication skills and presentation skills with a proven ability to understand key concepts and communicate effectively with technical staff, business stakeholders and senior management, as well as those who are less technical
We understand that not all candidates may meet the requirements listed above. If you believe you have the knowledge and experience necessary to excel in this role, we encourage you to apply.
What competencies will you need?
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- Change Management - Understands and recognizes the need for change; responds positively to new situations, obstacles and opportunities. Takes responsibility for inspiring, leading and executing change in an effective way
- Communication - Speaks, writes, listens and presents information in a logical and articulate manner appropriate to the audience; ensures information is shared and understood
- Creativity – Designs novel solutions to improve processes, systems, products and services
- Customer Focus - Strives to understand and fulfill the needs and expectations of internal and/or external customers
- People Leadership – Sets clear expectations and gives context; provides feedback and coaching to develop direct reports; motivates and recognizes exceptional performance
- Individual Development - Is self-motivated, has energy and drive, is self-aware, deals with challenges and takes ownership of continuous individual development
- Results Orientation - Holds self and/or others accountable for accomplishing work commitments and deliverables; understands the targeted results he/she is accountable for and actively strives to achieve them; sets high standards of performance
We are Offering...
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As part of our team here, as well as receiving a competitive base salary, you will also participate in a generous performance related bonus plan. In addition, you will also receive a 401k plan with company matching, Life Insurance, and Medical Insurance as well as 9 holidays.
The typical base salary range for this position is anticipated to be between $169,232 to $211,540.
Innovative - At ESI, we are committed to solving the complex and evolving needs of our customers through innovation and high-quality standards. We are focused on bringing cutting edge and environmentally sustainable solutions to the market. Our people are the critical resource required to make that happen. We support your success by creating a strong, inclusive culture, competitive total rewards, and an appropriate work-life balance.
Socially Responsible - We care about what you care about. We respect the individual differences that make up our unique expanding organization. We prioritize both sustainability and social impact in both our business operations and our local communities through our various ESI Cares initiatives and the ESI Foundation. There are many ways to get involved from employee network groups that support your interests and sense of belonging to paid volunteer days.
#LI-IF1
Equal Opportunity Employer
All qualified applications will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, pregnancy, genetic information, disability, status as a protected veteran, or any other protected category applicable under federal, state and local laws.
Salary Context
This $169K-$211K range is above the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Element Solutions Inc, 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 $154,000 based on 8,743 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600. This role's midpoint ($190K) sits 24% above the category median. Disclosed range: $169K to $211K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Element Solutions Inc AI Hiring
Element Solutions Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Piscataway, NJ, US, Norwalk, CT, US. Compensation range: $211K - $279K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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