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Mlops Engineer Jobs in Arizona (NOW HIRING)

OmegaHires

Lead Gen AI Engineer

Phoenix, AZ · On-site

$101K - $134K/yr

Lead Gen AI Engineer **Job Role:** Lead Gen AI Engineer with Python **Location:** Phoenix, AZ ... MLOps and AI model deployment. - Experience with Databricks, Snowflake, or enterprise data ...

OmegaHires

Gen AI Engineer with Python

Phoenix, AZ · On-site

Gen AI Engineer with Python We are looking for a Gen AI Engineer with strong expertise in Python ... Knowledge of MLOps and AI model deployment. Experience with Databricks, Snowflake, or enterprise ...

New

OmegaHires

Lead Gen AI Engineer with Python

Phoenix, AZ · On-site

$139K - $170K/yr

Lead Gen AI Engineer with Python We are looking for a Lead Gen AI Engineer with strong expertise in ... Knowledge of MLOps and AI model deployment. Experience with Databricks, Snowflake, or enterprise ...

New

Confiz Limited

Principal AI Engineer

Phoenix, AZ · On-site

$180 - $230/hr

Strong understanding of LLMs, RAG architectures, agentic AI systems, prompt engineering, model fine-tuning, and MLOps. * Databricks: Hands-on experience with Databricks Lakehouse (Delta Lake, Unity ...

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Showing results 21-40

All JobsMlops Engineer JobsMlops Engineer Jobs in Arizona

Mlops Engineer information

See Arizona salary details

$97.6K

$153.1K

$177.7K

How much do mlops engineer jobs pay per year?

As of Aug 15, 2026, the average yearly pay for mlops engineer in Arizona is $153,133.00, according to ZipRecruiter salary data. Most workers in this role earn between $147,088.00 and $165,049.00 per year, depending on experience, location, and employer.

Are MLOps engineers in demand?

MLOps engineers are in high demand due to the increasing adoption of machine learning and AI across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Docker and Kubernetes. The role offers strong job growth prospects and competitive salaries.

What is an MLOps engineer?

An MLOps Engineer is responsible for deploying, monitoring, and maintaining machine learning models in production. They bridge the gap between data science and operations by automating workflows, optimizing infrastructure, and ensuring model reliability. Their role includes CI/CD for ML models, data pipeline management, and performance monitoring. They also work with cloud platforms, containerization, and orchestration tools to scale ML systems efficiently.

What are some common challenges MLOps engineers face in their daily work?

Mlops Engineers often encounter challenges in integrating new machine learning models into existing production systems while ensuring minimal downtime and maintaining data integrity. Managing the scaling and orchestration of models across various cloud or on-prem environments can be complex, requiring close coordination with data scientists and DevOps teams. Staying up to date with rapidly evolving tools and best practices is also essential in this field. Addressing these challenges provides valuable opportunities to innovate and improve both technical processes and team collaboration.

What are the key skills and qualifications needed to thrive as an MLOps engineer, and why are they important?

To thrive as an Mlops Engineer, you need strong skills in software engineering, machine learning pipelines, and cloud infrastructure, often backed by a degree in computer science, engineering, or a related field. Familiarity with tools such as Docker, Kubernetes, TensorFlow, AWS/GCP/Azure, and CI/CD systems is essential, and certifications like AWS Certified Machine Learning or Kubernetes Administrator are often valued. Effective communication, problem-solving, and teamwork are crucial soft skills for collaborating across data science and IT teams. These abilities enable Mlops Engineers to efficiently deploy, manage, and scale machine learning models in dynamic production environments.

What does an MLOps engineer do?

An MLOps engineer is responsible for deploying, managing, and maintaining machine learning models in production environments. They work with tools like Docker, Kubernetes, and cloud platforms to automate workflows, ensure model reliability, and monitor performance. Their role combines software engineering, data science, and DevOps practices to streamline the deployment and lifecycle management of machine learning systems.

What are the most commonly searched types of Mlops Engineer jobs in Arizona?

The most popular types of Mlops Engineer jobs in Arizona are:

What are popular job titles related to Mlops Engineer jobs in Arizona?

For Mlops Engineer jobs in Arizona, the most frequently searched job titles are:

What job categories do people searching Mlops Engineer jobs in Arizona look for?

The top searched job categories for Mlops Engineer jobs in Arizona are:

What cities in Arizona are hiring for Mlops Engineer jobs?

Cities in Arizona with the most Mlops Engineer job openings:

Mlops Engineer jobs near you
Infographic showing various Mlops Engineer job openings in Arizona as of August 2026, with employment types broken down into 92% Full Time, 3% Part Time, and 5% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $153,133 per year, or $73.6 per hour.

Lead Gen AI Engineer

OmegaHires

Phoenix, AZ • On-site

$101K - $134K/yr

Contractor

Posted 6 days ago

Job description

Lead Gen AI Engineer
**Job Role:** Lead Gen AI Engineer with Python **Location:** Phoenix, AZ (Hybrid) **Job Description:** We are looking for a Lead Gen AI Engineer with strong expertise in Python, Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI to lead the design and development of enterprise AI solutions. The ideal candidate will have hands-on experience in preparing and engineering enterprise data for AI use cases, building scalable RAG pipelines, implementing autonomous AI agents, and integrating Large Language Models (LLMs) into production environments. This is a hands-on technical leadership role deep knowledge of AI architecture, data preparation, prompt engineering, vector databases, and modern AI frameworks. ** Skills** - 8+ years of software development experience with Python. - 3+ years of hands-on experience delivering Generative AI solutions. - Strong expertise in Retrieval-Augmented Generation (RAG) architecture and implementation. - Hands-on experience building Agentic AI solutions using LangGraph, CrewAI, AutoGen, LangChain, LlamaIndex, or Semantic Kernel. - Experience preparing, processing, and engineering structured and unstructured enterprise data for AI use cases. - Strong understanding of document ingestion, chunking strategies, embeddings, metadata enrichment, and vector search. - Experience with Vector Databases such as Pinecone, FAISS, ChromaDB, Weaviate, or Milvus. - Experience integrating LLMs including OpenAI, Azure OpenAI, Gemini, Claude, Llama, or Mistral. - Strong knowledge of Prompt Engineering, AI orchestration, and tool/function calling. - Experience developing APIs using FastAPI, Flask, or Django. - Experience with Docker, Kubernetes, Git, CI/CD, and cloud platforms (AWS, Azure, or GCP). **Preferred Skills** - Experience with AI evaluation frameworks, observability, and LLMOps. - Knowledge of MLOps and AI model deployment. - Experience with Databricks, Snowflake, or enterprise data platforms. - Familiarity with knowledge graphs and enterprise search solutions.

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