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Ai Monitoring Jobs (NOW HIRING)

Photon

Senior Agentic AI Engineer (Python)

Boston, MA · On-site

$132K - $177K/yr

Implement AI monitoring, observability, tracing, telemetry, and performance measurement. Also expected * Implement agent memory patterns (short-term, long-term, episodic). * Integrate agents with ...

ISACA

AI Solutions Engineer

Monitor, troubleshoot, and debug agentic workflows, application integrations, and AI-generated outputs. * Evaluate changes in LLM and AI technologies and recommend modifications or enhancements to ...

CYNET SYSTEMS

Lead AI Engineer

Santa Clara, CA · On-site

$70 - $75/hr

AI Monitoring and Observability. * Enterprise AI Architecture and Strategy. Benefits Our Benefits Include: * Medical, Dental, and Vision Insurance * 401(k) Retirement Plan * Health Savings Account ...

ISACA

AI Solutions Engineer

$98K - $148K/yr

Monitor, troubleshoot, and debug agentic workflows, application integrations, and AI-generated outputs. * Evaluate changes in LLM and AI technologies and recommend modifications or enhancements to ...

S R International, Inc.

Lead AI Engineer

$104K - $138K/yr

Experience training, deploying, and monitoring AI models * Strong communication, leadership, and technical mentoring skills Preferred Skills * Google Cloud AI Certifications * AWS AI Certifications

Koniag Services, Inc.

AI Governance Analyst

Washington, DC · On-site

$120 - $170/hr

Monitor and analyze developments in federal AI policy, legislation, executive orders, OMB guidance, and industry best practices, assessing their impact on SBA's AI governance program and supporting ...

New

Saim Technologies

Lead AI Engineer

Santa Clara, CA · On-site

$119K - $157K/yr

AI monitoring, observability, and performance management. * Knowledge of RAG architectures, vector databases, embeddings, and semantic search. * Experience with AI application evaluation, guardrails ...

kgs

AI Governance Analyst

Washington, DC · On-site

Monitor and analyze developments in federal AI policy, legislation, executive orders, OMB guidance, and industry best practices, assessing their impact on SBA's AI governance program and supporting ...

UnitedHealth Group

AI/ML Engineer

Eden Prairie, MN · Remote

$98K - $176K/yr

Design, develop, and deploy AI-powered solutions, LLM workflows, prompt chains, agents, RAG ... Monitor and continuously improve output quality, hallucination risk, relevance, accuracy ...

kgs

AI Governance Analyst

Washington, DC · On-site

Monitor and analyze developments in federal AI policy, legislation, executive orders, OMB guidance, and industry best practices, assessing their impact on SBA's AI governance program and supporting ...

Referrals Only

Lead AI Engineer

Concord, NC · On-site

$95K - $125K/yr

Skilled in LLMOps practices and monitoring production systems with observability tools ... Responsible Use of AI in Recruitment At Thoughtworks, we use AI tools to support our recruitment ...

Koniag, Inc.

AI Governance Analyst

Washington, DC · On-site

Monitor and analyze developments in federal AI policy, legislation, executive orders, OMB guidance, and industry best practices, assessing their impact on SBA's AI governance program and supporting ...

Lululemon

Senior AI/ML Engineer

Seattle, WA · On-site

$118K - $163K/yr

Developing reusable MLOps components to support experimentation, deployment, monitoring, and rollback * Partner with AI/ML scientists to productionize models while meeting accuracy, performance ...

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Ai Monitoring information

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How much do ai monitoring jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for ai monitoring in the United States is $16.90, according to ZipRecruiter salary data. Most workers in this role earn between $16.35 and $17.55 per hour, depending on experience, location, and employer.

What is AI monitoring?

AI monitoring refers to the process of continuously observing and analyzing artificial intelligence systems to ensure they operate as intended. This includes tracking performance, detecting anomalies, ensuring compliance with ethical guidelines, and identifying potential biases or errors. Effective AI monitoring helps organizations maintain transparency, improve system reliability, and ensure that AI models make fair and accurate decisions. It is essential in applications where AI impacts critical business or societal outcomes.

What are some common challenges faced by professionals in AI monitoring roles, and how can they be addressed?

Professionals in AI Monitoring often encounter challenges such as managing large volumes of data, identifying and responding to atypical model behavior, and ensuring compliance with ethical and regulatory standards. Staying updated on the latest AI trends and best practices, utilizing robust monitoring tools, and collaborating closely with data scientists and engineers can help address these challenges. Regular training and open communication within cross-functional teams are also essential to maintain effective oversight and quickly mitigate potential issues.

What are the key skills and qualifications needed to thrive as an AI monitoring specialist, and why are they important?

To thrive as an AI Monitoring Specialist, you need a solid understanding of data analysis, machine learning concepts, and system monitoring, often supported by a degree in computer science or a related field. Familiarity with monitoring platforms like Datadog, Prometheus, or Splunk, as well as experience with scripting languages and AI model management tools, is typically required. Attention to detail, critical thinking, and strong communication skills help specialists identify issues quickly and collaborate with technical teams. These skills and qualities are crucial for ensuring AI systems operate reliably, securely, and efficiently in real-world applications.

What is the difference between Ai Monitoring vs Data Analyst?

AspectAi MonitoringData Analyst
Required CredentialsTypically requires knowledge of AI systems, programming, and data analysis toolsRequires statistical, analytical, and data visualization skills, often with a degree in data science or related fields
Work EnvironmentOften involves monitoring AI systems in real-time, using specialized software, in tech or AI-focused companiesAnalyzes data sets, creates reports, and provides insights, working in various industries like finance, marketing, or healthcare
Employer & Industry UsageCommon in AI development firms, tech companies, and organizations deploying AI solutionsWidely used across industries for decision-making, reporting, and strategic planning

While both roles involve working with data, Ai Monitoring focuses on overseeing AI system performance and ensuring operational accuracy, whereas Data Analysts interpret data to support business decisions. Understanding these differences helps in choosing the right career path or job search focus.

More about Ai Monitoring jobs

What states have the most Ai Monitoring jobs?

States with the most job openings for Ai Monitoring jobs include:

What job categories do people searching Ai Monitoring jobs look for?

The top searched job categories for Ai Monitoring jobs are:

Ai Monitoring jobs near you
Infographic showing various Ai Monitoring job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 10% Part Time, and 5% Contract. Highlights an 92% Physical, 3% Hybrid, and 5% Remote job distribution, with an average salary of $35,147 per year, or $16.9 per hour.

Senior Agentic AI Engineer (Python)

Photon

Boston, MA • On-site

$132K - $177K/yr

Other

Posted 8 days ago

Job description

Hello ,

Hope you are doing well,

Myself Mankar from Photon and I have a position with our direct client, please send me your updated resume if you are interested.

Senior Agentic AI Engineer (Python)

Boston, MA- Onsite

Primary Objective

We are seeking a Senior Agentic AI Engineer to design, build, deploy, and operate enterprise-grade AI agents and multi-agent systems. The role focuses on Generative AI, LLMs, agentic workflows, RAG architectures, AI orchestration, and governed enterprise AI platforms delivering scalable copilots, intelligent automation, and knowledge systems across onshore and offshore delivery environments.

Success looks like:production-ready agents with measurable reliability, governed RAG integrated into enterprise systems, and clear observability/guardrails for business use cases.

Key Responsibilities

Primary

  • Design, develop, and deploy AI agents and multi-agent workflows using Python and agentic frameworks (e.g., LangChain, LangGraph, or equivalent).
  • Build enterprise-scale RAG solutions over structured and unstructured data sources.
  • Develop agent orchestration workflows integrating models, tools, APIs, and enterprise services.
  • Build AI-powered copilots, assistants, and automation solutions for enterprise use cases.
  • Implement AI monitoring, observability, tracing, telemetry, and performance measurement.

Also expected

  • Implement agent memory patterns (short-term, long-term, episodic).
  • Integrate agents with enterprise platforms (APIs, databases, SharePoint, Confluence, Salesforce, knowledge repositories).
  • Contribute to AI governance: guardrails, security controls, policy enforcement, and compliance.
  • Optimize prompts, reasoning strategies, workflows, and execution performance.
  • Collaborate with Data Science and ML teams on evaluation, optimization, and continuous improvement.

Must-Have Experience & Skills

  • 5 10 years of software engineering experience with strong Python expertise.
  • 3+ years designing and implementing AI/ML solutions.
  • Hands-on experience building Generative AI applications using LLMs, RAG, and agentic frameworks (LangChain/LangGraph or equivalent; OpenAI SDK or similar).
  • Experience delivering enterprise-grade AI solutions in production.
  • Experience with distributed onshore/offshore team delivery.
  • Solid API/service engineering fundamentals (REST/async services, integration patterns).
  • Practical understanding of AI observability, evaluation, and production reliability.

Preferred Skills

  • Cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI, or similar).
  • Vector databases / search (pgvector, OpenSearch, Pinecone, Weaviate, or similar).
  • LLMOps tooling (tracing, eval harnesses, prompt/version management).
  • Enterprise integrations (SharePoint, Confluence, Salesforce).
  • Containerized deployment practices (Docker; Kubernetes a plus).
  • AI security, PII handling, and guardrail frameworks.

Soft Skills

  • Clear written and verbal communication with technical and business stakeholders.
  • Ability to own delivery end-to-end in a distributed team model.
  • Pragmatic trade-off judgment between speed, quality, cost, and governance.

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