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Environmental Artificial Intelligence Jobs (NOW HIRING)

SCATR Corp.

Artificial Intelligence Engineer

Cleveland, OH

$111K - $133K/yr

... the world's most hostile environments, addressing a critical gap in Zero Trust security ... We are looking to add an experienced Artificial Intelligence (AI) Engineer to our dynamic team and ...

Nwacc

Foster a collaborative and inclusive classroom environment that encourages curiosity, innovation ... Demonstrated knowledge or experience with artificial intelligence concepts and tools (e.g., machine ...

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Environmental Artificial Intelligence information

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$51.5K

$100.1K

$148K

How much do environmental artificial intelligence jobs pay per year?

As of Jun 11, 2026, the average yearly pay for environmental artificial intelligence in the United States is $100,142.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,500.00 and $112,000.00 per year, depending on experience, location, and employer.

What is Environmental Artificial Intelligence?

Environmental Artificial Intelligence (Environmental AI) refers to the application of artificial intelligence technologies to address environmental challenges. This field uses machine learning, data analysis, and predictive modeling to monitor ecosystems, optimize resource use, track pollution, and support sustainable practices. Environmental AI can help governments, organizations, and researchers analyze large datasets from satellites, sensors, and other sources to make informed decisions about conservation and climate action. By automating data interpretation and prediction, Environmental AI enables more efficient and effective solutions to pressing environmental problems.

How does an Environmental Artificial Intelligence professional typically collaborate with interdisciplinary teams to develop impactful solutions?

Environmental Artificial Intelligence professionals often work closely with environmental scientists, data engineers, policy experts, and software developers to design and implement AI-driven solutions for sustainability challenges. Collaboration involves regular meetings to align on project goals, exchanging domain-specific knowledge, and integrating diverse data sources. This interdisciplinary teamwork ensures that AI models are both scientifically rigorous and practically applicable, leading to more robust and impactful environmental outcomes. Building strong communication skills and being open to feedback are essential for success in these collaborative settings.

What are the key skills and qualifications needed to thrive as an Environmental Artificial Intelligence Specialist, and why are they important?

To thrive as an Environmental Artificial Intelligence Specialist, you need a strong background in environmental science, data analysis, and machine learning, often supported by a relevant degree in computer science, environmental engineering, or a related field. Proficiency with programming languages like Python or R, experience using AI frameworks (such as TensorFlow or PyTorch), and familiarity with GIS systems are typically required. Strong problem-solving skills, effective communication, and collaboration across multidisciplinary teams are crucial soft skills. These competencies enable specialists to design effective AI solutions for complex environmental challenges, ensuring accurate data-driven insights and impactful sustainability initiatives.

What is the difference between Environmental Artificial Intelligence vs Environmental Data Analyst?

AspectEnvironmental Artificial IntelligenceEnvironmental Data Analyst
Required CredentialsDegree in Computer Science, AI, or Environmental Science; knowledge of AI and machine learningDegree in Environmental Science, Data Analysis, or related field; proficiency in data analysis tools
Work EnvironmentResearch labs, tech companies, environmental agencies using AI modelsEnvironmental consulting firms, government agencies analyzing environmental data
Employer & Industry UsageTech firms, environmental startups, research institutionsGovernment agencies, environmental consultancies, NGOs
Common Search & Comparison IntentUnderstanding AI's role in environmental workAnalyzing environmental data for insights

Environmental Artificial Intelligence focuses on developing AI models to solve environmental problems, requiring skills in AI and machine learning. Environmental Data Analysts interpret environmental data to inform decisions, requiring strong data analysis skills. While both roles work within the environmental sector, AI specialists develop algorithms, whereas data analysts focus on data interpretation.

Infographic showing various Environmental Artificial Intelligence job openings in the United States as of June 2026, with employment types broken down into 75% Full Time, and 25% Contract. Highlights an 75% In-person, and 25% Hybrid job distribution, with an average salary of $100,142 per year, or $48.1 per hour.

Artificial Intelligence Engineer

Prophecy Technologies

West Des Moines, IA • On-site

Full-time

Posted 2 days ago

Job description

Role Name: Artificial Intelligence Engineer
Location: West Des Moines-Iowa
Job Summary
We are seeking an experienced Artificial Intelligence Engineer to design, develop, and scale modern AI solutions leveraging GenAI, LLMs, and Agentic AI. The ideal candidate will work across data, engineering, and business teams to deliver intelligent applications, RAG pipelines, AI agents, and document automation workflows using AWS-native AI services.
Experience Range (Required Skills)
  • 5+ years in software engineering or data science.
  • 1+ year hands-on experience with GenAI, LLMs, or Agentic AI.
  • Proficiency in Python, REST APIs, and JavaScript/TypeScript frameworks (React, Node.js).
  • Experience working with cloud-native infrastructure: Docker, CI/CD pipelines.
  • Hands-on experience with AWS AI/ML services, including:
  • Bedrock
  • SageMaker
  • Textract
  • Translate
  • Lambda & other serverless components

Key Responsibilities
  • Design, build, and scale RAG pipelines, vector search architectures, and LLM evaluation workflows.
  • Develop intelligent AI agents using LangChain, AutoGen, LlamaIndex, or LangGraph.
  • Ingest, process, and prepare structured/unstructured data-chunking, embeddings, parsing, retrieval.
  • Create intuitive front-end experiences powered by LLM and agent-based intelligence.
  • Integrate enterprise AI tools such as:
  • Claude
  • GPT-4/4o / ChatGPT Enterprise
  • Copilot
  • Glean
  • Shelf
  • Drive advanced document automation, including summarization, translation, and named-entity recognition (NER).
  • Implement observability, governance, and responsible AI practices in compliance with enterprise regulations.
  • Partner with cross-functional teams (IT, data, risk, compliance, business) to deliver user-facing AI solutions.

Required Skills & Experience
  • Hands-on experience developing AI solutions using GenAI/LLM frameworks.
  • Strong expertise in Python and API development (REST).
  • Experience with modern frontend concepts (React, Node.js).
  • Knowledge of embeddings, vector stores, retrieval workflows, and LLM orchestration.
  • Experience with AWS AI services and cloud-native development patterns.

Competencies
  • Strong problem-solving and analytical thinking.
  • Excellent communication and collaboration across technical & business stakeholders.
  • Ability to work in fast-paced, innovation-driven environments.
  • Self-starter with strong ownership of solution delivery.

Preferred Skills
  • Experience with LangChain, AutoGen, LlamaIndex, or LangGraph.
  • Prior exposure to enterprise AI platforms such as Glean or Shelf.
  • Understanding of responsible AI, security, governance, and observability frameworks.
  • Experience building UI/UX for AI-driven applications.

Apply