ZipRecruiter

Log In

1

Llmops Jobs (NOW HIRING)

Gartner

Lead AI Engineer (ML Ops)

Irving, TX · Hybrid

$95K - $125K/yr

High proficiency in MLOps and LLMOps platforms (e.g., MLflow, Kubeflow, Weights & Biases). * Strong DevOps background, including hands-on experience with containerization (Docker, Kubernetes) and CI ...

Manulife

Senior AI Engineer

Boston, MA · On-site

$113K - $155K/yr

Stay current with emerging trends in AI, ML, GenAI, LLMOps, software engineering, cloud platforms, and financial services technology, and share relevant learnings with the team. * Mentor junior ...

Gartner

Lead AI Engineer (ML Ops)

Stamford, CT · Hybrid

$109K - $143K/yr

High proficiency in MLOps and LLMOps platforms (e.g., MLflow, Kubeflow, Weights & Biases). * Strong DevOps background, including hands-on experience with containerization (Docker, Kubernetes) and CI ...

Gartner

Lead AI Engineer (ML Ops)

Stamford, CT · Hybrid

$109K - $144K/yr

High proficiency in MLOps and LLMOps platforms (e.g., MLflow, Kubeflow, Weights & Biases). * Strong DevOps background, including hands-on experience with containerization (Docker, Kubernetes) and CI ...

Gartner, Inc.

Lead AI Engineer (ML Ops)

Irving, TX · On-site

$98K - $129K/yr

High proficiency in MLOps and LLMOps platforms (e.g., MLflow, Kubeflow, Weights & Biases). * Strong DevOps background, including hands-on experience with containerization (Docker, Kubernetes) and CI ...

Gartner

Lead AI Engineer (ML Ops)

Irving, TX · Hybrid

$98K - $129K/yr

High proficiency in MLOps and LLMOps platforms (e.g., MLflow, Kubeflow, Weights & Biases). * Strong DevOps background, including hands-on experience with containerization (Docker, Kubernetes) and CI ...

Merican

AWS AI Engineer

Herndon, VA · On-site

Implement and manage LLMOps workflows, including deployment, monitoring, and scaling of Generative AI systems * Build and maintain agent-based workflows using frameworks like LangChain, CrewAI, or ...

next page

Showing results 1-20

All JobsLlmops Jobs

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.

More about Llmops jobs
What cities are hiring for Llmops jobs? Cities with the most Llmops job openings:
What states have the most Llmops jobs? States with the most job openings for Llmops jobs include:
What job categories do people searching Llmops jobs look for? The top searched job categories for Llmops jobs are:
Llmops jobs near you

Machine Learning Operations (MLOps) Engineer - AWS (with LLM Focus)

Kaav Inc.

Miramar, FL

$64K - $87K/yr

Other

Posted 5 days ago

Job description

Responsibilities:

  • LLM-Optimized MLOps Infrastructure: Design and implement MLOps infrastructure on AWS tailored for LLMs, leveraging services like SageMaker, EC2 (with GPU instances), S3, ECS/EKS, Lambda, and more.
  • LLM Deployment Pipelines: Build and manage CI/CD pipelines specifically for LLM deployment, addressing unique challenges like model size, inference optimization, and versioning.
  • LLMOps Practices: Implement LLMOps best practices for monitoring model performance, drift detection, prompt management, and feedback loops for continuous improvement.
  • RESTful API Development: Design and develop RESTful APIs to expose LLM capabilities to other applications and services, ensuring scalability, security, and optimal performance.
  • Model Optimization: Apply techniques like quantization, distillation, and pruning to optimize LLM models for efficient inference on AWS infrastructure.
  • Monitoring and Observability: Establish comprehensive monitoring and alerting mechanisms to track LLM performance, latency, resource utilization, and potential biases.
  • Prompt Engineering and Management: Develop strategies for prompt engineering and management to enhance LLM outputs and ensure consistency and safety.
  • Collaboration: Work closely with data scientists, researchers, and software engineers to integrate LLM models into production systems effectively.
  • Cost Optimization: Continuously optimize LLMOps processes and infrastructure for cost-efficiency while maintaining high performance and reliability.
Qualifications:
  • Experience: 3+ years of experience in MLOps or a related field, with hands-on experience in deploying and managing LLMs.
  • AWS Expertise: Strong proficiency in AWS services relevant to MLOps and LLMs, including SageMaker, EC2 (with GPU instances), S3, ECS/EKS, Lambda, and API Gateway.
  • LLM Knowledge: Deep understanding of LLM architectures (e.g., Transformers), training techniques, and inference optimization strategies.
  • Programming Skills: Proficiency in Python and experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation), REST API frameworks (e.g., Flask, FastAPI), and LLM libraries (e.g., Hugging Face Transformers).
  • Monitoring: Familiarity with monitoring and logging tools for LLMs, such as Prometheus, Grafana, and CloudWatch.
  • Containerization: Experience with Docker and container orchestration (e.g., Kubernetes, ECS) for LLM deployment.
  • Problem Solving: Excellent problem-solving and troubleshooting skills in the context of LLMs and MLOps.
  • Communication: Strong communication and collaboration skills to effectively work with cross-functional teams.

Required Skills : GenAI
Additional Skills : AI Developer

Apply