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About This Role
The Opportunity:
Tetra Tech is adding an innovative and strategic *AI Lead* to drive the adoption and advancement of Artificial Intelligence (AI) across our Consulting \& Engineering Services (CES) business. This leadership role will be responsible for developing and executing an enterprise AI strategy that enhances client delivery, improves operational efficiency, supports business development, and fosters innovation across environmental consulting, engineering, permitting, infrastructure, and corporate operations.
The AI Lead will collaborate with technical leaders, operations managers, marketing teams, and business stakeholders to identify high\-value AI opportunities, oversee solution development, establish governance standards, and promote responsible AI adoption throughout the organization.
This is an exciting opportunity to shape the future of consulting by integrating AI technologies into the services we provide to clients while enhancing the way we work. Location: Flexible within the Continental United States (Hybrid) Why Tetra Tech:
At Tetra Tech, we are Leading with Science® to solve the world's most complex challenges. Our industry\-leading experts in engineering and consulting are committed to driving positive change in communities around the world. For over 50 years, we have been at the forefront of innovation and sustainability. Today, we stand as a market leader, offering cutting\-edge solutions in water, environment, energy, and international development. Our work has improved more than 625 million lives around the world.
As Tetra Tech continues to advance digital innovation, this role offers a unique opportunity to help shape the future of our Consulting \& Engineering Services (CES) business by leveraging Artificial Intelligence to enhance client solutions, improve project delivery, and drive operational excellence across the organization. Your Impact:
Join Tetra Tech to make a real difference. Our work leverages cutting\-edge technologies, advanced analytics, and the expertise of world\-class scientists and engineers to create meaningful change around the world. Discover your full potential – join us to advance your career while leaving a lasting legacy. Essential Job Functions:AI Strategy \& Innovation* Develop and implement CES's enterprise AI strategy and multi\-year roadmap.
- Identify opportunities to integrate AI technologies across environmental consulting, engineering, permitting, field services, project management, business development, and corporate operations.
- Evaluate emerging AI technologies and recommend solutions that improve productivity, quality, and client outcomes.
- Establish AI governance, security, risk management, and responsible\-use standards aligned with company policies.
AI Solutions \& Implementation* Lead the development, testing, deployment, and continuous improvement of AI\-enabled business solutions.
- Oversee AI pilots and enterprise deployments utilizing Generative AI, Large Language Models (LLMs), machine learning, automation, and intelligent workflows.
- Partner with business and technical teams to streamline proposal development, technical reporting, environmental compliance documentation, project delivery, and knowledge management.
- Support AI integration with Microsoft Copilot, Azure AI, OpenAI technologies, Power Platform, and other approved enterprise solutions.
Business Partnership \& Change Management* Collaborate with business leaders to identify high\-value AI use cases that improve consulting services and internal operations.
- Lead cross\-functional teams to implement AI initiatives that deliver measurable business value.
- Develop training materials, best practices, and adoption strategies to promote AI literacy throughout CES.
- Build and mentor an internal AI Community of Practice that encourages collaboration and innovation.
Performance \& Continuous Improvement* Establish performance metrics to measure AI adoption, operational efficiency, return on investment, and client impact.
- Monitor emerging technologies, regulatory developments, and industry trends to ensure CES remains at the forefront of AI innovation.
- Promote continuous improvement and knowledge sharing across the organization.
- Perform additional duties as assigned.
Required Qualifications:* Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Environmental Science, Business, or a related discipline.
- 7\+ years of experience in Artificial Intelligence, Machine Learning, Data Analytics, Digital Transformation, or related technical leadership roles.
- Demonstrated experience leading enterprise technology or AI initiatives.
- Strong understanding of:
+ Large Language Models (LLMs)
+ Generative AI
+ Prompt Engineering
+ Retrieval\-Augmented Generation (RAG)
+ AI Governance
- Experience with Microsoft Azure AI, Microsoft Copilot, Power Platform, OpenAI technologies, or comparable enterprise AI platforms.
- Experience leading cross\-functional teams and managing complex technology initiatives.
- Excellent communication, presentation, stakeholder engagement, and change management skills.
- Must possess a valid driver's license with a clean driving record without restrictions.
Preferred Qualifications:* Experience within environmental consulting, engineering consulting, infrastructure, utilities, energy, or related industries.
- Familiarity with GIS platforms, environmental datasets, permitting workflows, sustainability reporting, and regulatory compliance.
- Master's degree in Computer Science, Data Science, Engineering, Environmental Science, Business, or a related field.
- Experience implementing AI solutions for proposal automation, technical reporting, document management, knowledge management, or project delivery.
- Familiarity with responsible AI practices, model governance, cybersecurity, and data privacy.
- Experience mentoring technical teams and leading enterprise innovation initiatives.
Success Measures:
Within the first year, the AI Lead will:* Develop and implement an enterprise AI strategy for CES.
- Successfully deploy multiple high\-value AI use cases across business operations and client services.
- Improve employee productivity through AI\-enabled workflows and automation.
- Establish AI governance, security, and responsible\-use standards.
- Demonstrate measurable operational efficiencies and return on AI investments.
- Foster an organizational culture that embraces AI innovation and continuous learning.
Life at Tetra Tech:
The perks of working at Tetra Tech include:* Comprehensive and market\-competitive benefits. https://www.tetratech.com/careers/life\-at\-tetra\-tech/
- Merit\-based financial rewards.
- Flexibility and company\-wide commitment to work/life balance.
- Collaborative team atmosphere that values the contributions of all employees.
- Learning and development opportunities for ongoing professional growth.
A pre\-employment drug screen in compliance with federal regulations is required, along with a physical if needed. Compensation:
Pay commensurate with experience.
Pay Range: $175,000\-$195,000 annually Pay Range \- There are multiple factors that are considered in determining final pay for a position, including, but not limited to, relevant work experience and demonstrated work experience in the above role; skills, certifications, and competencies that align to the specified role; geographic location; and education, as well as contract provisions regarding labor categories that are specific to the position. *At Tetra Tech, health and safety play a vital role in our success. Tetra Tech’s employees work together to comply with all applicable health \& safety practices and protocols, including public health orders and regulations that are mandated by local, state, provincial, federal, international authorities, and clients.*
About Tetra Tech:
Tetra Tech is the leader in water, environment, and sustainable infrastructure, providing high\-end consulting and engineering services for projects worldwide. With 30,000 employees working together, Tetra Tech provides clear solutions to complex problems by *Leading with Science**®* to address the entire water cycle, protect and restore the environment, design sustainable and resilient infrastructure, and support the clean energy transition.
Tetra Tech is proud to be an Equal Opportunity Employer. All qualified candidates will be considered without regard to race, color, religion, national origin, age, disability, sex, marital or familial status, status as a protected veteran, or any other characteristic protected by law. Tetra Tech is a VEVRAA federal contractor and we request priority referral of veterans.
We invite applications from all interested parties.
Explore our open positions at https://www.tetratech.com/careers Follow us on social media to learn more about our people, culture, and opportunities:
LinkedIn: @TetraTechCareers
X (Twitter): @TetraTechJobs
Additional Information
- Organization: 194 CES
Salary Context
This $175K-$195K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Tetra Tech, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($185K) sits 14% below the category median. Disclosed range: $175K to $195K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Tetra Tech AI Hiring
Tetra Tech has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Parsippany-Troy Hills, NJ, US. Compensation range: $195K - $195K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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