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Assistant Mlops Jobs (NOW HIRING)

Juvare

$125K - $160K/yr

Experience supporting AI/MLOps workflows is a plus. Location * Atlanta / Remote Must Have * Cloud ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

Juvare

Senior DevOps Engineer

Atlanta, GA · On-site +1

$125K - $160K/yr

Experience supporting AI/MLOps workflows is a plus. Location * Atlanta / Remote Must Have * Cloud ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

Juvare

Senior DevOps Engineer

Atlanta, GA · On-site +1

$125K - $160K/yr

Experience supporting AI/MLOps workflows is a plus. Location * Atlanta / Remote Must Have * Cloud ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

OVA.Work

Conversational AI Developer

... virtual assistants. The role involves collaborating with various teams to create scalable ... cloud and MLOps practices. • Troubleshoot production issues and continuously optimize model ...

LMI

... * Assist product leads in translating operational needs and feedback into actionable technical requirements and strategies. * Mentor junior team members, guiding their ML and MLOps skill development ...

LMI

ML Ops Engineer - Clearance Required

Pittsburgh, PA · On-site

... * Assist product leads in translating operational needs and feedback into actionable technical requirements and strategies. * Mentor junior team members, guiding their ML and MLOps skill development ...

QVC

AI Enterprise Architect

$70.75 - $91/hr

... MLOps, LLMOps, generative AI, large language models, retrieval-augmented generation, AI assistants, and agent-based workflows. • Familiarity with retail and digital commerce ecosystems, including ...

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Assistant Mlops information

What is an Assistant MLOps?

Assistant MLOps are professionals who support the deployment, monitoring, and management of machine learning models in production environments. They assist senior MLOps engineers with tasks like automating workflows, managing data pipelines, maintaining infrastructure, and ensuring model performance. Their role bridges the gap between data science and IT operations, helping organizations scale and maintain their AI solutions efficiently. Assistant MLOps often have knowledge of cloud services, CI/CD tools, and basic programming, and they work closely with data scientists and engineers.

What is the difference between Assistant Mlops vs Data Engineer?

AspectAssistant MlopsData Engineer
Required CredentialsCertifications in cloud platforms, basic scripting, ML toolsComputer science degree, SQL, Python, data architecture
Work EnvironmentCollaborates with ML teams, supports deployment pipelinesBuilds data pipelines, manages databases, processes large datasets
Industry UsageAI/ML projects, cloud-based environmentsData infrastructure, analytics, big data solutions

Assistant Mlops and Data Engineer roles share overlapping skills in cloud platforms and scripting. However, Assistant Mlops focuses on supporting ML deployment and operations, while Data Engineers primarily build and maintain data infrastructure. Both roles are essential in data-driven organizations but serve different functions within the data ecosystem.

What are the typical daily responsibilities of an Assistant MLOps?

As an Assistant MLOps professional, you can expect your daily tasks to involve supporting the deployment, monitoring, and maintenance of machine learning models in production environments. This often includes collaborating with data scientists to automate model training and testing workflows, managing cloud-based resources, and ensuring that data pipelines are running smoothly. You'll also help troubleshoot issues related to model performance or infrastructure and assist in implementing best practices for version control and continuous integration. Working closely with both engineering and data teams, you'll play a key role in ensuring that ML models remain reliable and scalable in real-world applications.

What are the key skills and qualifications needed to thrive as an Assistant MLOps?

To thrive as an Assistant MLOps, you need a solid understanding of machine learning fundamentals, programming (especially Python), and experience with cloud platforms; a degree in computer science or a related field is typically preferred. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, and version control systems (e.g., Git) is important, and certifications in cloud services (AWS, Azure, GCP) can be advantageous. Strong problem-solving, communication, and collaboration skills help you bridge the gap between data science and operations teams. These combined skills ensure efficient deployment, monitoring, and maintenance of machine learning models in production environments.

Is assistant MLOps in high demand?

Assistant MLOps roles are increasingly in demand as organizations expand their machine learning and AI initiatives. These positions often require knowledge of cloud platforms, automation tools, and deployment pipelines, reflecting the growing need for scalable and reliable ML systems across industries.
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Assistant Mlops jobs near you
Infographic showing various Assistant Mlops job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, 1% Temporary, and 2% Contract. Highlights an 98% Physical, 1% Hybrid, and 1% Remote job distribution.

AI Foundation Model Engineer (LLM / Agentic AI / Full-Stack AI Engineering)

Niche IT Software Solutions LLC

Jersey City, NJ • On-site

Other

Posted 9 days ago

Job description

AI Foundation Model Engineer

LLM / Agentic AI / Full-Stack AI Engineering
Level: Senior Individual Contributor
Experience: 9+ years

Role Overview

We are seeking a Senior AI Foundation Model Engineer to build and deploy secure, scalable, enterprise-grade AI solutions using LLMs, RAG and agentic workflows. The role involves developing production AI applications and reusable services for an AWS-hosted, cloud-agnostic AI platform.

Key Responsibilities

· Build LLM applications, RAG pipelines, knowledge assistants, document intelligence solutions and workflow agents.

· Develop embeddings, semantic search, reranking, grounding and citation capabilities.

· Deploy and manage AI services using APIs, Docker, Kubernetes, CI/CD and cloud-native infrastructure.

· Collaborate on Terraform/IaC, environment promotion, release controls and rollback procedures.

· Optimize models and inference for accuracy, latency, throughput, token usage, reliability and cost.

· Implement LLMOps/MLOps covering evaluation, monitoring, observability, feedback loops and continuous improvement.

· Ensure security, privacy, Responsible AI, governance and audit readiness.

· Maintain production documentation, runbooks and release records.

Required Skills

· Strong hands-on experience with LLMs, transformers, GenAI, RAG, embeddings and vector databases.

· Advanced Python skills and experience with PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, Semantic Kernel or similar frameworks.

· Production deployment experience using APIs, containers, Kubernetes, CI/CD and monitoring tools.

· Practical AWS AI/cloud experience, preferably with Bedrock, SageMaker, OpenSearch, Lambda and EKS/ECS.

· Working knowledge of Terraform/IaC, MLOps/LLMOps, model evaluation, inference optimization and secure data handling.

Preferred Experience

· Banking, risk, compliance, financial crime or enterprise technology experience.

· Experience with Kendra, Azure OpenAI, Vertex AI, Databricks, vLLM, Triton, MLflow, Kubeflow or model gateways.

· Knowledge of LoRA, PEFT, instruction tuning, quantization, model governance and private/open-source LLM deployments.

Alternate Titles: LLM Engineer, GenAI Engineer, AI Platform Engineer, RAG Engineer, Applied ML Engineer or NLP Engineer.

Apply