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Mlflow Jobs in Arizona (NOW HIRING)

Confiz Limited

Principal AI Engineer

Phoenix, AZ · On-site

$180 - $230/hr

Define data architecture and pipelines using Databricks (Delta Lake, Unity Catalog, MLflow) for large-scale data processing and model training/serving. * Architect full-stack solutions that integrate ...

Intraedge

AI/ML Engineer

Phoenix, AZ · On-site

Exposure to MLOps, workflow orchestration, and data-processing technologies, such as MLflow, Kubeflow, Argo Workflows, Kafka, Spark, or Apache NiFi.

New

Insight Enterprises

Principal Data Scientist

Phoenix, AZ · On-site

$150 - $210/hr

Build and guide ML solutions on Databricks, using notebooks, feature engineering, MLflow, Delta Lake, and scalable data pipelines to support healthcare AI and analytics use cases.End-to-End ML ...

Inficare Technologies

AI Python Engineer

Phoenix, AZ · On-site

MLflow, Kubeflow, Argo Workflows, Kafka, Spark, or NiFi Preferred Skills * Flask, FastAPI, Hugging Face, Triton Inference Server * pgVector, Milvus, Prometheus, Elasticsearch, Kibana * CI/CD: Jenkins ...

onsemi

Sr. Manager AI & Data Science

Scottsdale, AZ

... MLflow. Your expertise in transformer-based models, retrieval-augmented generation (RAG), and vector databases will be instrumental in driving innovation. But your true impact will come from ...

onsemi

Sr. Manager AI & Data Science

Scottsdale, AZ · On-site

... MLflow. Your expertise in transformer-based models, retrieval-augmented generation (RAG), and vector databases will be instrumental in driving innovation. But your true impact will come from ...

MY DR NOW

AI & Machine Learning Engineer

Chandler, AZ · On-site

$100K - $110K/yr

TensorFlow, PyTorch, Scikit-learn, MLflow * Epic Clarity, Epic Caboodle, FHIR, HL7 * Claude Code, GitHub Copilot, Cursor * Git, Azure DevOps, CI/CD, Docker, Kubernetes Company Description MY DR NOW ...

Phoenix Staff

Lead Data & AI Engineer

Phoenix, AZ · On-site +1

$50 - $60/hr

Experience with Epic or Cerner integrations, HEDIS or risk adjustment programs, MLOps tools such as MLflow or GitHub Actions, Power BI semantic modeling, and relevant Snowflake or Microsoft ...

Master Electronics

Data Scientist II

Phoenix, AZ · On-site

Develop scalable data pipelines on Databricks; integrate experimentation and ML systems with modern data and MLOps platforms (Databricks, MLflow); establish CI/CD pipelines, version control, testing ...

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Mlflow information

What is the difference between Mlflow vs Data Scientist?

AspectMlflowData Scientist
Required CredentialsKnowledge of machine learning tools, Python, and data managementDegree in Data Science, Statistics, or related field; programming skills
Work EnvironmentData science teams, machine learning projects, software developmentResearch, data analysis, model development, cross-functional teams
Employer & Industry UsageTech companies, AI startups, data-driven organizationsVarious industries including tech, finance, healthcare, and retail

While Mlflow is a platform for managing the machine learning lifecycle, a Data Scientist focuses on analyzing data and building models. Mlflow tools support Data Scientists in tracking experiments, but the roles differ in scope and responsibilities.

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Mlflow jobs near you
Infographic showing various Mlflow job openings in Arizona as of August 2026, with employment types broken down into 58% Full Time, 22% Temporary, and 20% Contract. Highlights an 80% In-person, and 20% Remote job distribution.

AI Engineer with Security Clearance

Agensys Corporation

Phoenix, AZ • On-site

Other

Re-posted 6 days ago

Job description

Overview: We are looking for an AI/ML Engineer to develop, implement, and scale machine learning and generative AI solutions. This position blends strong software engineering expertise with practical experience building applications powered by modern AI frameworks and large language models. Required Skills Software Engineering * Proficient in Python and SQL; experience with Java or JavaScript is a plus * Basic front-end knowledge including HTML and CSS * Experience developing APIs using frameworks such as FastAPI * Familiarity with containerization and deployment using Docker Machine Learning Fundamentals * Hands-on experience developing and deploying machine learning models, including deep learning solutions * Strong understanding of model selection, evaluation techniques, and performance tuning * Experience operationalizing ML pipelines and monitoring model performance in production Frameworks & Tools * Practical experience working with LLM APIs and orchestration tools such as LangChain or LangGraph * Familiarity with platforms like Azure ML, Snowflake Cortex, and MLflow for managing the ML lifecycle Modern AI Concepts * Knowledge and experience with: * Vector databases and semantic search techniques * Retrieval-Augmented Generation (RAG) * Embeddings, chunking methods, and reranking strategies * Frameworks used to evaluate LLM performance * Tool usage and agent-based architectures * Optimizing cost and latency for AI-driven workloads Ideal Candidate * Demonstrates ownership across the full lifecycle, from initial prototype through production release * Comfortable building scalable AI-driven systems that deliver measurable business outcomes * Strong communicator who can clearly convey technical ideas in a business context * Candidates with a combination of strong engineering fundamentals and hands-on generative AI experience, especially with RAG architectures and production-level LLM applications-will be particularly competitive. Minimum Qualifications * Strong proficiency with Python, SQL, FastAPI, and Docker * Experience integrating LLM APIs and working with at least one orchestration framework (LangChain or LangGraph) * Exposure to enterprise AI platforms such as Azure ML, Snowflake Cortex, or MLflow

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