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Full Time Ai Agent Jobs (NOW HIRING)

New York, United States Type: Full-time Department: Technology Job Summary We are seeking a Senior ... Build and orchestrate multi-agent pipelines -including supervisor agents, collaborative agent ...

We're looking for a full-time AI Engineer to help shape and execute Fanatics' AI strategy across ... You'll own problems end-to-end: define them, design the solution, build the agent or workflow ...

... full time internship based out of our Boulder office. PROJECTS * Advisor Copilot (Multi-Agent ... AI Agent Platform & Infrastructure Architect a scalable multi-agent platform with orchestration ...

AI Engineer

San Francisco, CA · On-site

$150K - $250K/yr

We're hiring a full-time AI Engineer to own the prompts, agents, evals, and pipelines behind user ... Iterate fast on prompts, agent designs, and orchestration patterns. * Partner with the Product ...

EEO/About Us Benefits Along with competitive pay, as a full-time Infosys employee you are also ... We do it by enabling the enterprise with an AI-powered core that helps prioritize the execution of ...

We're looking for a full-time AI Engineer to help shape and execute Fanatics' AI strategy across ... You'll own problems end-to-end: define them, design the solution, build the agent or workflow ...

Type: Full-time About Us Prosper is building the most advanced conversational AI agents for the ... You will own a portfolio of AI agent deployments across multiple customers and act as the general ...

AI Engineer

Denver, CO · On-site

$130K - $170K/yr

DR Kharon is seeking a full-time AI Engineer based in Denver. This role requires in-office ... Claude Agent SDK, LangChain, AutoGPTI) * Take features from idea to production, including ...

Santa Clara, CA (Onsite) Employment Type: Full-Time Visa Type: Not Specified Must-Have ... Strong understanding of LLM workflows, AI orchestration, multi-agent systems, and AI automation ...

Showing results 21-40

Full Time Ai Agent information

What is a full time AI agent?

Full Time AI Agents are advanced artificial intelligence systems designed to autonomously perform tasks, solve problems, or assist users on a continuous basis within an organization. Unlike traditional AI tools that require human initiation, these agents operate proactively, handling responsibilities such as data analysis, customer support, process automation, and more. They are designed to work alongside human teams, increasing efficiency and allowing employees to focus on higher-level tasks. Full Time AI Agents utilize machine learning, natural language processing, and other AI technologies to adapt and improve over time.

How does a full time AI agent typically collaborate with cross-functional teams within an organization?

Full Time AI Agents often work closely with data scientists, engineers, product managers, and business stakeholders to design, implement, and refine AI solutions. Collaboration usually involves regular meetings to align on project goals, sharing progress updates, and integrating AI models into broader business workflows. Strong communication skills are essential, as you may need to explain complex AI concepts to non-technical colleagues and ensure solutions meet both technical and business requirements. This collaborative environment helps drive innovation and ensures the successful deployment of AI-driven products.

What are the key skills and qualifications needed to thrive as a full time AI agent, and why are they important?

To thrive as a Full-Time AI Agent, you need a strong background in computer science, machine learning, and data analysis, often supported by a relevant degree or equivalent experience. Mastery of programming languages like Python, familiarity with AI frameworks (such as TensorFlow or PyTorch), and experience with cloud platforms are typically required. Excellent problem-solving abilities, adaptability, and effective communication set top candidates apart in this field. These skills are crucial for developing, deploying, and maintaining AI solutions that drive business value and innovation.

What is the difference between Full Time Ai Agent vs Customer Support Specialist?

AspectFull Time Ai AgentCustomer Support Specialist
CredentialsBasic technical knowledge, training in AI toolsCustomer service skills, communication training
Work EnvironmentRemote, AI-driven platformsOffice or remote, direct customer interaction
Industry UsageTech, e-commerce, AI companiesRetail, telecom, service industries
Search/Comparison IntentUnderstanding AI automation rolesCustomer service roles and skills

Full Time Ai Agents primarily focus on managing AI-powered customer interactions, often working remotely with technical training. Customer Support Specialists handle direct customer inquiries, emphasizing communication skills. While both roles involve customer interaction, Full Time Ai Agents are more tech-oriented, whereas Customer Support Specialists focus on personal service.

More about Full Time Ai Agent jobs

What cities are hiring for Full Time Ai Agent jobs?

Cities with the most Full Time Ai Agent job openings:

What are the most commonly searched types of Ai Agent jobs?

The most popular types of Ai Agent jobs are:

What states have the most Full Time Ai Agent jobs?

States with the most job openings for Full Time Ai Agent jobs include:

What are popular job titles related to Full Time Ai Agent jobs?

For Full Time Ai Agent jobs, the most frequently searched job titles are:

Infographic showing various Full Time Ai Agent job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 77% Full Time, 19% Part Time, and 3% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution.

Senior AI Developer

Manhattan, NY

$125K - $140K/yr

Full-time

Re-posted 25 days ago


Job description

Senior AI Developer

Location: New York, United States

Type: Full-time

Department: Technology

Job Summary

We are seeking a Senior AI Developer to support one of our premier clients-a leading global financial institution-with strong expertise in building intelligent AI agents and components that can reason, plan, and act autonomously. The ideal candidate will have hands-on experience developing scalable multi-agent AI systems using modern orchestration frameworks such as LangChain and LangGraph, integrating agentic workflows end-to-end, and shipping production-grade AI applications.

Responsibilities

  • Design and develop AI agents and autonomous multi-agent systems using modern agentic frameworks including LangGraph and LangChain, with the ability to architect agent graphs, define node transitions, and manage stateful agent workflows
  • Build and orchestrate multi-agent pipelines-including supervisor agents, collaborative agent networks, and hierarchical agent architectures-to solve complex, multi-step financial use cases
  • Implement guardrails, reasoning workflows, and ReAct-based patterns within LangChain/LangGraph to improve reliability, decision-making, and agent safety
  • Develop memory management (short-term, long-term, episodic) and tool-use capabilities (MCP, LangChain Tools, custom tool integrations) for AI agent systems
  • Leverage LangGraph's stateful graph execution model to build resilient, interruptible, and human-in-the-loop agentic workflows
  • Integrate LLM-powered agents with external APIs, databases, and enterprise data platforms via LangChain's retrieval, routing, and chain composition primitives
  • Partner closely with prompt engineers, data scientists, and platform teams to optimize AI application performance across multi-agent deployments
  • Build and maintain scalable Python-based services, APIs, and microservices that serve as agent execution environments and tool backends
  • Develop and support AIOps capabilities and CI/CD pipelines for AI agent deployment, versioning, and monitoring (including LangSmith or equivalent observability tooling)
  • Work with modern data platforms including Snowflake, Databricks, and Lakehouse architectures as grounding and tool-use data sources for agents
  • Ensure AI agent solutions are scalable, secure, observable, and production-ready

Eligibility Requirements

  • 8+ years of overall software engineering experience, with a strong focus on AI/ML systems in recent years
  • Hands-on production experience with LangChain - including chains, agents, tools, retrievers, memory modules, and prompt templates
  • Hands-on production experience with LangGraph - including stateful graph construction, conditional edges, checkpointing, human-in-the-loop interrupts, and multi-agent graph topologies
  • Demonstrated experience designing and deploying multi-agent systems - including orchestrator/worker patterns, agent-to-agent communication, task delegation, and shared state management
  • Experience implementing guardrails, ReAct patterns, and chain-of-thought reasoning within agentic pipelines
  • Strong understanding of agent memory architectures (in-context, vector-store-backed, episodic) and tool-use patterns (function calling, MCP, LangChain tool wrappers)
  • Familiarity with LangSmith or equivalent observability/tracing platforms for debugging and monitoring agent behaviour in production
  • Strong Python engineering skills including async programming, APIs, and microservices
  • Experience with AIOps and CI/CD pipeline development for AI agent deployment and lifecycle management
  • Hands-on experience with Snowflake, Databricks, and Lakehouse architectures
  • Strong understanding of scalable distributed systems and cloud-native application development
  • Strong communication and cross-functional collaboration skills
  • Nice to Have
    • Experience with other agentic frameworks such as AutoGen, CrewAI, or OpenAI Assistants API
    • Familiarity with LangGraph Cloud or self-hosted LangGraph Server for agent deployment
    • Background in financial services AI applications (risk, compliance, trading, operations)
    • Experience with vector databases (Pinecone, Weaviate, pgvector) as long-term memory stores for agents
    • Contributions to open-source LangChain/LangGraph ecosystem 

In the US, the target base salary for this role is $125,000-$140,000. Compensation is based on a range of factors that include relevant experience, knowledge, skills, other job-related qualifications, and geography. We expect the majority of candidates who are offered roles at our company to fall throughout the range based on these factors

How to Apply

  • Click "Apply Now" to submit your resume through our career site
  • Be sure to include any relevant experience that aligns with the role.
  • Qualified candidates will be contacted by a member of our recruitment team for next steps

About eClerx

eClerx is a leading provider of productized services, bringing together people, technology and domain expertise to amplify business results.
The firm provides business process management, automation, and analytics services to a number of Fortune 2000 enterprises, including some of the world's leading financial services, communications, retail, fashion, media & entertainment, manufacturing, travel & leisure, and technology companies. Incorporated in 2000, eClerx is traded on both the Bombay and National Stock Exchanges of India. The firm employs more than 19,000 people across Australia, Canada, France, Germany, Switzerland, Egypt. India, Italy, Netherlands, Peru, Philippines, Singapore, Thailand, the UK, and the USA.

For more information, visit www.eclerx.com  

You can also find us on:

https://www.linkedin.com/company/eclerx/ 

https://www.indeed.com/cmp/Eclerx/about

https://www.glassdoor.com/eClerx

eClerx is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law. We are also committed to protecting and safeguarding your personal data. Please find our policy here