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Llmops Jobs (NOW HIRING)

TheStaffed

Applied AI Engineer

New York, NY · On-site

This role offers the opportunity to architect enterprise-grade GenAI workflows, establish LLMOps best practices, and evolve into a platform owner for shared AI standards. Responsibilities ...

Cisco

AI LLMOps Engineering Technical Leader

San Jose, CA · On-site

$216K - $280K/yr

The application window is expected to close on: 10/30/2026 Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received . Meet the Team The team ...

Cisco

AI LLMOps Engineering Technical Leader

San Jose, CA · On-site

$216K - $280K/yr

The application window is expected to close on: 10/30/2026 Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received . Meet the Team The team ...

CINC Systems

Senior LLM Ops Engineer

$107K - $146K/yr

Senior LLMOps Engineer (Remote - Global - Work From Anywhere) About CINC Systems CINC Systems is the leading provider of accounting and management software for the community association management ...

2T Consulting

$121K - $159K/yr

MLOps/LLMOps: CI/CD, Monitoring, Observability, Model Deployment, Governance

CINC Systems

Senior LLM Ops Engineer

$107K - $146K/yr

Senior LLMOps Engineer (Remote - Global - Work From Anywhere) About CINC Systems CINC Systems is the leading provider of accounting and management software for the community association management ...

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Llmops information

What is the difference between Llmops vs Data Scientist?

AspectLlmopsData Scientist
Required credentialsKnowledge of machine learning, AI frameworks, cloud platformsStatistics, programming, data analysis skills
Work environmentAI/ML teams, cloud environments, deployment pipelinesData analysis, modeling, reporting in various industries
Employer usageTech companies, AI startups, research labsFinance, healthcare, tech, retail

While both roles involve working with data and machine learning, Llmops focuses on deploying and maintaining large language models in production environments, requiring expertise in AI infrastructure. Data Scientists primarily analyze data, build models, and generate insights. Llmops professionals ensure models operate efficiently at scale, whereas Data Scientists develop the models and interpret results.

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Infographic showing various Llmops job openings in the United States as of September 2026, with employment types broken down into 94% Full Time, and 6% Contract. Highlights an 73% Physical, 9% Hybrid, and 18% Remote job distribution.

Applied AI Engineer

New York, NY • On-site

TheStaffed
See more jobs at TheStaffedexternal

Contractor

Re-posted 18 days ago

Job description

Our client is seeking an Applied AI Engineer with 10+ years of experience to design, build, and operationalize generative AI solutions across their Lending business lines. This role offers the opportunity to architect enterprise-grade GenAI workflows, establish LLMOps best practices, and evolve into a platform owner for shared AI standards.
Responsibilities & Qualifications
  • Design and evolve reusable GenAI workflows and agentic systems deployed across multiple Lending business units
  • Develop enterprise-grade AI-powered document ingestion, data extraction, and content generation capabilities
  • Build AI assistants embedded in Lending systems using agentic workflows and orchestration patterns
  • Deliver automated workflows for content generation, reporting, and approval processes
  • Establish and operationalize LLMOps practices including extraction accuracy monitoring, prompt management, and audit controls
  • Design and implement security controls for entitlements, PII handling, and governance within AI systems
  • Provide expert technical guidance on GenAI architecture, model selection, and platform orchestration decisions
  • Act as hands-on technical expert with a clear advancement path to platform owner for enterprise GenAI standards

Requirements
  • 10+ years of software engineering experience with demonstrated expertise in generative AI solutions
  • Strong proficiency with LangChain, LLM platforms, and RAG (Retrieval-Augmented Generation) architectures
  • Production-level experience with Python or Java and building scalable ML systems
  • Hands-on expertise in agentic workflows, vector search, prompt management, and data ingestion pipelines
  • Deep understanding of LLMOps practices including model monitoring, logging, and production reliability
  • Experience designing and implementing data pipelines and working with large language models in enterprise environments
  • Strong grasp of AI security, compliance, and governance requirements for financial services or similarly regulated industries

Apply