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Autonomous Ai Jobs (NOW HIRING)

KL Software Technologies Pvt. Ltd.

AI Architect

Newark, NJ · On-site

$200 - $250/hr

You will design, deploy, and operate multiple fleets of autonomous AI agents, powered by Anthropic Claude (Claude Code) and/or OpenAI Codex, that build, test, and ship software features, run product ...

Fairygodboss

Agentic AI Developer

Columbia, MD · On-site

$80 - $100/hr

Design and develop autonomous AI agents capable of reasoning, planning, and executing tasks independently. * Build and orchestrate multi-agent workflows using modern AI frameworks and tooling.

Argo Cyber Systems

AI/ML Automation Engineer

Arlington, VA · On-site

$120K - $135K/yr

Develop intelligent automation using LLMs, foundation models, and autonomous AI agents.Build Retrieval-Augmented Generation (RAG) pipelines for secure knowledge retrieval.Design prompt engineering ...

Accord Technologies Inc.

Lead AI Engineer

Piscataway, NJ · On-site

$104K - $137K/yr

Experience building agentic workflows or autonomous AI agents * Exposure to code generation / code fixing using LLMs * Experience with vector databases * Good knowledge of AWS services (Lambda, ECS ...

Northern Base

AI/LLM Engineer

New York, NY · On-site

Autonomous AI Agents * ReAct (Reasoning + Acting) * LLM Reasoning, Planning & Task Execution * Model Context Protocol (MCP) * Tool Calling & Tool Integration * LLM Workflow Chaining & Orchestration

Alterion

Senior Applied AI Engineer

San Francisco, CA · On-site

$125 - $150/hr

About Alterion Alterion is building the control plane for the agentic enterprise - enabling organizations to safely deploy, monitor, and scale autonomous AI agents with full visibility and governance.

Weld County

AI Solutions Analyst

Greeley, CO · On-site

$90K - $127K/yr

Design, build, and deploy agentic AI workflows that autonomously execute multi-step business processes using Microsoft Copilot Studio, Power Automate, and Azure AI services. * Identify and evaluate ...

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Autonomous Ai information

See salary details

$26.5K

$119.8K

$197K

How much do autonomous ai jobs pay per year?

As of Sep 9, 2026, the average yearly pay for autonomous ai in the United States is $119,841.00, according to ZipRecruiter salary data. Most workers in this role earn between $77,000.00 and $155,500.00 per year, depending on experience, location, and employer.

What is Autonomous AI?

Autonomous AI refers to artificial intelligence systems capable of making decisions and taking actions without human intervention. These systems use machine learning, perception, and reasoning to independently complete tasks in dynamic environments. Examples include self-driving cars, industrial robots, and virtual assistants that learn and adapt over time. The goal of autonomous AI is to reduce the need for manual control, increase efficiency, and improve safety in various applications.

What are the key skills and qualifications needed to thrive as an Autonomous AI engineer?

To thrive as an Autonomous AI Engineer, a strong background in computer science, machine learning, and robotics, typically supported by a relevant degree, is essential. Familiarity with programming languages like Python and C++, frameworks such as TensorFlow or PyTorch, and experience with automation systems or simulation environments are typically required. Creative problem-solving, adaptability, and strong communication skills help individuals excel in collaborative and rapidly-evolving technical teams. These skills ensure the development of reliable, safe, and innovative autonomous AI solutions that meet complex real-world challenges.

What are some of the typical challenges faced when working in an Autonomous AI role, and how can job seekers prepare for them?

Professionals in Autonomous AI roles often encounter challenges related to the complexity of integrating AI systems with real-world environments. This can include ensuring safety, reliability, and scalability of algorithms when deployed in dynamic settings such as robotics or autonomous vehicles. Job seekers should be prepared to collaborate closely with cross-functional teams, including engineers, data scientists, and domain experts, and stay updated on the latest advancements in AI ethics, regulation, and technology. Proactively engaging in hands-on projects and continuous learning will help address these challenges and foster successful contributions to the field.

What is the difference between Autonomous Ai vs Data Scientist?

AspectAutonomous AiData Scientist
Required CredentialsTypically a degree in computer science, AI, or related fields; certifications in AI and machine learningDegree in statistics, computer science, or related fields; certifications in data analysis and machine learning
Work EnvironmentDeveloping and deploying AI systems, often in tech companies or research labsAnalyzing data, building models, and providing insights, mainly in corporate or research settings
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, marketing, and tech industries

While Autonomous Ai focuses on creating and managing autonomous AI systems, Data Scientists analyze data to inform decision-making. Both roles require strong technical skills and knowledge of machine learning, but their daily tasks and objectives differ significantly.

What do autonomous AI agents do?

Autonomous AI agents are software systems designed to perform tasks independently without human intervention. They can analyze data, make decisions, and execute actions in environments such as robotics, virtual assistants, or automated trading, often utilizing machine learning and AI algorithms to adapt and improve their performance over time.

What other helpful pages are available for Autonomous Ai?

Other pages related to Autonomous Ai:

Infographic showing various Autonomous Ai job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 76% Full Time, 19% Part Time, and 4% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $119,841 per year, or $57.6 per hour.

AI Architect

Newark, NJ • On-site

KL Software Technologies Pvt. Ltd.
See more jobs at KL Software Technologies Pvt. Ltd.external

$200 - $250/hr

Other

Posted 7 days ago

Key responsibilities

  • Design, deploy, and operate multiple fleets of autonomous AI agents to build, test, and ship software features, run product management workflows, and execute digital marketing campaigns.

  • Build and manage coding agents that autonomously generate, review, validate, and commit code based on work items from Azure DevOps, ensuring quality and adherence to requirements.

  • Develop and oversee product management and digital marketing agents that autonomously handle backlog grooming, requirement refinement, prioritization, and campaign content generation.

Job description

KL Software Technologies(“KLST”) iis hiring a hands-on AI Architect to lead our transformation into an agentic-AI-first product engineering organization. You will design, deploy, and operate multiple fleets of autonomous AI agents, powered by Anthropic Claude (Claude Code) and/or OpenAI Codex, that build, test, and ship software features, run product management workflows, and execute digital marketing campaigns across our flagship KLST products (learn more here www.klstinc.com/whyklstforlegal ).

This is NOT a chatbot or data-science role. You will industrialize software delivery using agentic AI developer tools – Claude Code, AI code review and optimization tools (e.g., ponytail), multi-agent product build orchestrators (e.g., gstack, which spins up CEO / PM / BA / QA / Dev agents that work together to deliver a complete product), and free / lower-cost open-source options such as NVIDIA’s agentic AI toolkits (NeMo Agent Toolkit and NIM microservices).

The Mission

Stand up and operate AT LEAST FIFTY (50) 24/7 autonomous coding and QA agents delivering product features across KLST products by the end of the year – organized so that each Senior Engineer owns and manages 5–10 agents, reviews every agent’s output, and validates results BEFORE any code is allowed to be committed.

Key Responsibilities:
  • Architect, deploy, and scale a multi-agent software delivery platform using Claude Code, agentic code review/optimization tools (ponytail or similar), product build orchestrators (gstack or similar), and NVIDIA’s open-source agentic toolkits – selecting the right mix of commercial and free/open-source tooling to control cost.
  • Design end-to-end agent workflows covering the full SDLC – requirements, design, coding, code review, QA automation, and release – with human-in-the-loop approval gates at every commit.
  • Build coding agents that autonomously pull work items/tickets from Azure DevOps Boards, generate implementation code with Claude Code and/or OpenAI Codex to meet each ticket’s requirements, self-QA the code against positive and negative test cases, and check the validated code into the repository.
  • Build product management agents that autonomously groom the backlog, draft and refine requirements and user stories, prioritize work, and generate release notes and status reporting.
  • Build digital marketing agents that autonomously plan, generate, and optimize marketing content and campaigns – SEO, email, social, and web – tied to product launches and releases.
  • Define and enforce the agent governance model: mandatory Senior Engineer review and validation of agent output before commits, branch protection, audit trails, rollback, and security / IP safeguards for AI-generated code.
  • Enable and coach Senior Engineers to become “agent managers”, each owning and supervising 5–10 agents; build the playbooks, prompt libraries, guardrails, and evaluation metrics they use to review and validate agent output.
  • Integrate the agent fleet with our Git / CI-CD pipelines (Azure DevOps / GitHub) so agents work 24/7 within guardrails across netDocShare, imDocShare, and KLapper repositories.
  • Continuously measure and report agent fleet productivity, code quality, defect escape rates, and cost per feature; optimize model/toolkit selection (Claude vs. open source / NVIDIA) for cost and performance.
  • Stay current with the agentic AI ecosystem (Model Context Protocol, multi-agent orchestration, agent evaluation frameworks) and continuously upgrade KLST’s Agentic Delivery Platform.
Key Qualifications: Required Skills
  • Very strong, demonstrable background setting up and operating MULTIPLE autonomous AI agents in production – designing agent architectures, orchestrating agent-to-agent collaboration, and running fleets of agents 24/7 with reliability and guardrails.
  • Overall, at least TEN (10) years “hands-on” software engineering experience with a strong full-stack background (.NET / TypeScript / React or Angular / REST APIs / SQL) on Azure or AWS.
  • Minimum TWO (2) years of hands-on experience building with LLMs and agentic AI developer tools – Claude Code and/or OpenAI Codex (or GitHub Copilot / Cursor / Windsurf), prompt engineering, and LLM APIs (Anthropic, OpenAI, Google, or open-weight models).
  • Hands-on experience with multi-agent orchestration frameworks and toolkits – e.g., gstack, ponytail, NVIDIA NeMo Agent Toolkit, LangGraph, AutoGen, or CrewAI – including agent-to-agent workflows (PM / BA / Dev / QA agent roles).
  • Hands-on experience building autonomous coding agents that ingest tickets / work items from Azure DevOps, generate code with Claude Code and/or OpenAI Codex to meet the requirements, validate it against positive and negative test cases, and commit the code – end-to-end with minimal human intervention.
  • Experience building autonomous agents beyond software delivery – product management agents (backlog grooming, requirements / user-story generation, prioritization, reporting) and digital marketing agents (content generation, campaign planning, SEO / email / social execution).
  • Strong experience with automated code review and QA automation – unit / integration / end-to-end testing (Playwright, Selenium, or similar) and using AI agents to author and execute test suites – including both positive and negative test cases – before code is committed.
  • Strong DevOps skills: Git branching and PR workflows, CI/CD (Azure DevOps or GitHub Actions), containerization, and secrets/access management for autonomous agents.
  • Proven ability to define engineering governance for AI-generated code: review gates, quality metrics, traceability, and compliance controls.
  • Strong presentation and communication skills (both written and verbal) are required; able to train and influence senior engineers to adopt the agent-manager operating model.
Preferred Skills
  • Experience with Model Context Protocol (MCP) servers, RAG pipelines, and agent evaluation / benchmarking frameworks.
  • Experience building product management agents with tools such as Azure DevOps Boards, Jira, or Aha! automating backlog grooming, roadmap updates, and stakeholder reporting.
  • Experience building digital marketing agents across SEO, content / CMS, email automation, and social platforms – connecting marketing workflows to product launches and releases.
  • Knowledge of the Microsoft 365 / SharePoint ecosystem and legal document management platforms (iManage, NetDocuments), the domain of netDocShare and imDocShare.
  • Experience optimizing LLM spend, prompt caching, model routing, and running open-weight models on NVIDIA GPUs as a lower-cost alternative to commercial APIs.
  • Microsoft Azure, AWS, or NVIDIA certifications.
  • Willing to travel nationally or internationally on temporary and permanent assignments (United States, Australia, India).
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