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Llmops Jobs (NOW HIRING)

TRM Labs

Senior MLOps Engineer, LLMOps

San Francisco, CA · On-site

$200 - $220/hr

As a Senior MLOps Engineer with a focus in LLMOps , you'll be at the core of building and scaling the technical infrastructure for AI/ML systems. You will: * Build reusable CI/CD workflows for model ...

Rapid Eagle Inc

AWS Healthcare Data Engineer

$55 - $70/hr

  • Medical

  • Dental

  • Retirement

Implement LLMOps for deployment, monitoring, evaluation, and model lifecycle management. * Develop cluster and job orchestration for large-scale processing. * Build automated CI/CD pipelines for ML ...

RAPID EAGLE INC

AWS Healthcare Data Engineer

Charlotte, NC · Remote

$55 - $70/hr

  • Medical

  • Dental

  • Retirement

Implement LLMOps for deployment, monitoring, evaluation, and model lifecycle management. * Develop cluster and job orchestration for large-scale processing. * Build automated CI/CD pipelines for ML ...

Lorven Technologies

Cloud Platform Engineer

Charlotte, NC · On-site

$55.75 - $74/hr

GenAI Platforms / LLMs RAG (Retrieval Augmented Generation) MLOps / LLMOps pipelines Key Responsibilities (Keywords for Search): Build enterprise cloud platforms (GCP + Azure) Implement Terraform ...

Jj

Director R&D AI Systems

Raritan, NJ

Key ResponsibilitiesMLOps and LLMOps Platform Management * Lead strategy, operations, and adoption for Cross R&D MLOps and LLMOps platforms, and approved enterprise model lifecycle tooling.

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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 August 2026, with employment types broken down into 77% Full Time, 2% Temporary, and 21% Contract. Highlights an 77% In-person, 4% Hybrid, and 19% Remote job distribution.

Gen AI / ML Full Stack Engineer, orchestration, Python/Java, RAG, LLMOps, Vectors, 12+ Mths Cont NYC

ZnA Inc

New York, NY • On-site

Contractor

Re-posted 9 days ago

Job description

 Gen AI / ML Full Stack Engineer, orchestration, Python/Java, RAG, LLMOps, Vectors, 12+ Mths Cont NYC

JPC – 3566

Level 3 : 5 to 8  Years of Industry exp

LOCATION:   New York ( 3 days hybrid, inperson interview will be needed ) 

Duration : 12+ months

Title: Gen AI / ML Full Stack Engineer , orchestration framework (Langchains etc), Python/Java, RAG, LLMOps, Adv Vectors (ColBERT/chunking), production failures, Fixed Income / Lending Platforms 12+ Mths Cont NYC

Description:

 Applied AI Engineer Overview

Morgan Stanleys Fixed Income Institutional Lending Technology team is building an enterprise grade GenAI workflow platform to enable document data extraction, embedded productivity assistants, and automated business workflows across business lines.

This is not a research or demo role.

We are seeking senior hands-on full stack engineers who have designed, built, and operated GenAI systems in production, and understand failure modes, evaluation, and governance as first class AI-powered systems.

What Youll Do Design and evolve reusable GenAI workflows used across Lending business lines.

  • Develop an enterprise grade AI-based document ingestion and data extraction capability, including traceability, confidence scoring, and human-in-the-loop review.
  • Build AI-powered assistants embedded in Lending systems using agentic workflows.
  • Deliver automated content and deck generation workflows for reporting and approvals.
  • Provide expert advice on GenAI architecture including model selection, orchestration patterns, and evaluation strategy.
  • Establish LLMOps practices: extraction accuracy, assistant reliability, prompts management, and audit monitoring.
  • Design and implement controls for entitlements, PII handling within open-source models in a regulated environment.
  • In the role you are expected to act as a hands-on technical expert, and it has a clear path to becoming a platform owner responsible for shared GenAI standards across Lending.

What Youll Bring

  • 2+, dedicated experience in practical application of GenAI solutions in an enterprise business environment.
  • Designing and operating GenAI orchestration frameworks in production beyond vendor examples (e.g., LangChain systems),
  • 5+ years of strong front-to-back engineering experience, focusing on AI ML platforms and workflows (Python or Java).
  • Proven experience building and operating production grade GenAI / LLM platforms, applying patterns such as RAG, tool/function calling, agentic workflows, and validated structured outputs.
  • Strong LLMOps expertise, including evaluation harnesses, prompt and version management, regression testing, observability, and reliability measurement in production systems.
  • Hands on experience building AI-first data ingestion pipelines with measurable quality, accuracy, and reliability.
  • Advanced retrieval experience advanced vector search, including multi vector and late interaction approaches (e.g., ColBERT, chunking), multi stage retrieval pipelines, metadata filtering, reranking.
  • Solid understanding of evaluation metrics and how they shape practical RAG system design (e.g., recall vs precision, latency vs quality, MRR, NDCG).
  • Experience operating GenAI systems through real production failures (model regressions, retrieval degradation, prompt drift, data quality issues) and designing mitigation strategies.
  • Nice to Have Fixed Income or Institutional Lending domain experience. Experience working in regulated environments with strong audit and control requirements.
  • Familiarity with enterprise security, data governance, and entitlement models.
  • Experience designing reusable internal platforms or shared developer tooling. Frontend experience is beneficial (Angular or React)