Interested in this AI/ML Engineer role at Environmental Resources Management?
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About This Role
Principal Consultant – Global AI \& Data Services
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Lead the future of sustainability through data, analytics, and AI.
At ERM, this role is not about incremental improvement—it is about defining how the world’s leading organizations use AI and data to solve their most complex environmental, health, safety, and sustainability challenges. As a Managing / Principal Consultant within Global AI \& Data Services, you will operate at the intersection of strategy, technology, and impact—shaping solutions that matter at enterprise and global scale.
Why This Role Matters
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Organizations are facing unprecedented expectations around sustainability, transparency, and digital maturity. AI and advanced data platforms are no longer “nice to have”—they are essential infrastructure for responsible growth. This role sits at the core of ERM’s digital strategy, enabling clients to turn complex environmental data into trusted insights, scalable systems, and intelligent decision‑making engines that drive real\-world outcomes.
What Your Impact Is
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- Influence how global organizations design and operationalize AI‑enabled EHS\&S and ESG systems
- Shape enterprise‑grade data architectures, governance models, and analytics strategies
- Enable smarter, faster, and more defensible sustainability decisions through advanced analytics and agentic AI
- Mentor teams, elevate technical excellence, and help grow ERM’s Global AI \& Data Services capability
- Build lasting client relationships rooted in trust, innovation, and measurable impact
Key Responsibilities
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- Lead the design and delivery of advanced analytics, visualization, and AI solutions for global clients
- Develop interactive dashboards and insights using Power BI, Tableau, and Looker Studio
- Architect and oversee data integrations and migrations across platforms such as EQuIS, Enablon, and modern cloud environments (Azure, AWS, GCP)
- Design and maintain scalable data pipelines using SQL, Python, and ETL frameworks
- Partner with clients to define data models, governance frameworks, data quality, and security standards
- Design and implement agentic AI workflows and AI‑enabled use cases supporting EHS\&S and sustainability objectives
- Support environmental data analysis across groundwater, surface water, air quality, and biological metrics
- Act as a trusted advisor to senior client stakeholders, translating business strategy into digital solutions
- Contribute to practice development, internal knowledge sharing, and innovation initiatives
What You’ll Bring
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### Required
- Bachelor’s or Master’s degree in Data Analytics, Environmental Science, Computer Science, or a related field
- 6\-8\+ years of experience in data analytics, data engineering, environmental data management, or consulting
- Proven expertise in Power BI, Tableau, SQL, and Python
- Hands‑on experience with cloud platforms (Azure, AWS, GCP)
- Strong understanding of modern data architectures (e.g., Medallion Architecture) and data engineering best practices
- Excellent communication skills with the ability to engage technical and non‑technical stakeholders
- Strong project leadership and client engagement capabilities
- Valid driver’s license and good driving record
- Authorization to work in the United States without immigration sponsorship
### Preferred
- Experience delivering digital solutions within EHS, ESG, or sustainability domains
- CDMP, AWS, or Azure Data Engineer certification
- Experience with data governance tools (e.g., Alation, Collibra)
- Knowledge of ESG reporting frameworks and sustainability performance metrics
- Familiarity with Agile delivery models or Lean Six Sigma methodologies
- Experience building production‑grade ML, LLM, and agentic AI systems, including OCR, RAG, and tool/function calling
- Knowledge of MLOps/LLMOps, vector databases, AI security, and privacy
- Experience with environmental data platforms such as EQuIS, Enablon, or similar systems
- Ready to Lead Digital Transformation That Matters? Join ERM and help global organizations leverage Salesforce technology to drive smarter decisions, measurable outcomes, and sustainable impact—at scale.
- For thePrincipal Consultant – Global AI \& Data Servicesposition, the anticipated annual base pay is $140,000–$155,000 USD. Actual pay will depend on factors such as education, experience, skills, location, performance, and business needs. In some cases, pay may fall outside this range. This role may be eligible for bonus pay (casual and fixed term/flex force employees are not bonus eligible).
We offer a comprehensive benefits package, including paid time off, parental leave, medical, dental, vision, life, disability, AD\&D insurance, 401(k) or RRSP/DPSP, and other applicable benefits to eligible employees.
- Note: Bonuses, commissions, and other forms of additional compensation are not guaranteed and subject to the sole discretion of ERM and its policies and procedures.
*Who We Are:*
As the largest global pure play sustainability consultancy, we partner with the world’s leading organizations, creating innovative solutions to sustainability challenges and unlocking commercial opportunities that meet the needs of today while preserving opportunity for future generations.
At ERM we know that creating a diverse, equitable and inclusive work environment is an essential part of making our company a great place to build a career. We also see our diversity as a strength that helps us create better solutions for our clients. Our diverse team of world\-class experts supports clients across the breadth of their organizations to operationalize sustainability, underpinned by our deep technical expertise in addressing their environmental, health, safety, risk and social issues. We call this capability our “boots to boardroom” approach for its comprehensive service model that allows ERM to develop strategic and technical solutions that advance objectives on the ground or at the executive level.
Please submit your resume and brief cover letter.
ERM does not accept recruiting agency resumes. Please do not forward resumes to our jobs alias, ERM employees or any other company location. ERM is not responsible for any fees related to unsolicited resumes.
- ERM is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, gender, sexual orientation, gender identity, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. Thank you for your interest in ERM!
Salary Context
This $140K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Environmental Resources Management, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($147K) sits 18% below the category median. Disclosed range: $140K to $155K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Environmental Resources Management AI Hiring
Environmental Resources Management has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US. Compensation range: $155K - $155K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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