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Ml Inference Jobs in Arizona (NOW HIRING)

Wise Skulls

AI/ML Data Scientist

Phoenix, AZ · On-site

AI/ML Data Scientist Location: Phoenix, AZ - (100% Onsite) Duration: 6 months (possibility of an ... Strong understanding and practical application of causal inference and causal reasoning ...

Donato Technologies Inc

AI Engineer Agentic AI

Phoenix, AZ · On-site

$121.60K - $160.40K/yr

AI/ML: RAG, Agentic AI, LLM integration, embeddings, fine-tuning, inference tooling * ML Stack: PyTorch, Hugging Face, TensorFlow exposure * Validation/State Management: Pydantic, Zod Required ...

New

Clifton Larson Allen

Lead ML Ops engineer

Tempe, AZ

$98.20K - $129.30K/yr

Oversee enterprisescale AI platforms supporting model training, inference, evaluation, monitoring ... Leadershiplevel expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.

Deloitte

AI Engineer Consultant

Tempe, AZ · Hybrid

Deliver governed data and features for ML/GenAI (curated datasets, feature pipelines/serving) supporting training and real-time inference, including consistency, caching, backfills, and latency SLOs.

Deloitte

AI Engineer Senior Consultant

Tempe, AZ · Hybrid

$100.10K - $137.40K/yr

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). * Implement safety, privacy, and ...

Deloitte

AI Data Engineer - Senior Consultant

Tempe, AZ · Hybrid

$100.10K - $137.40K/yr

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). * Implement safety, privacy, and ...

Deloitte

AI Engineer Senior Consultant

Tempe, AZ · Hybrid

$100.10K - $137.40K/yr

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). A successful candidate would possess ...

Deloitte

AI Data Engineer - Senior Consultant

Tempe, AZ · On-site

$103.10K - $140.10K/yr

... ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). • Implement safety, privacy, and access controls (PII handling, prompt-injection defenses ...

Inficare Technologies

Lead AI Engineer

Phoenix, AZ · On-site

$99.80K - $131.40K/yr

LLM infrastructure, inference, and model gateways * Evaluation, observability, and safety tooling ... LangGraph, LangChain, AirFlow, etc Agentic AI and ML * Integration of commercial and open-source ...

Lorven Technologies

AI Solution Architect

Tempe, AZ · On-site

$60.25 - $79.50/hr

This individual will operate at the intersection of architecture, AI platform engineering, ML ... Real-time inference pipelines * Ensure architectural alignment with: * Cloud strategy * Enterprise ...

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Ml Inference information

What are the key skills and qualifications needed to thrive in ML Inference, and why are they important?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.

What are some common challenges faced by ML Inference Engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What are popular job titles related to Ml Inference jobs in Arizona? For Ml Inference jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Ml Inference jobs in Arizona look for? The top searched job categories for Ml Inference jobs in Arizona are:
What cities in Arizona are hiring for Ml Inference jobs? Cities in Arizona with the most Ml Inference job openings:
Ml Inference jobs near you
Infographic showing various Ml Inference job openings in Arizona as of May 2026, with employment types broken down into 22% Internship, and 78% Full Time. Highlights an 40% In-person, and 60% Remote job distribution.
AI/ML Data Scientist

AI/ML Data Scientist

Wise Skulls

Phoenix, AZ • On-site

Contractor

Posted 7 days ago

Job description

Title: AI/ML Data Scientist
Location: Phoenix, AZ - (100% Onsite)
Duration: 6 months (possibility of an extension)
Implementation Partner: Infosys
End Client: To be disclosed
JD:
Position Overview
  • We are seeking a highly experienced Senior Data Scientist to support and enhance our AIOps (Artificial Intelligence for IT Operations) solution. This position plays a critical role in advancing our anomaly detection, root cause analysis, and intelligent automation capabilities across enterprise systems.
  • The ideal candidate will bring deep expertise in machine learning, statistical modeling, and large-scale data analysis, with strong hands-on proficiency in Python and SQL. This individual will drive innovation in operational intelligence by leveraging anomaly detection, causal reasoning, time series modeling, and emerging GenAI techniques.

Key Responsibilities
  • Design and implement scalable machine learning models for AIOps use cases including anomaly detection and root cause analysis.
  • Develop and optimize advanced anomaly detection algorithms for infrastructure, application, and operational telemetry data.
  • Apply causal reasoning frameworks to identify drivers of incidents and operational disruptions.
  • Build and deploy time series forecasting and modeling solutions to predict performance degradation and system failures.
  • Develop robust data pipelines and analytical workflows using Python and SQL.
  • Integrate Generative AI (GenAI) techniques for intelligent summarization, incident triage, knowledge extraction, and automation.
  • Collaborate with engineering, DevOps, and platform teams to operationalize ML models in production environments.
  • Drive continuous improvement of model performance, scalability, and reliability.
  • Mentor junior data scientists and contribute to best practices in MLOps and model governance.

Required Qualifications
  • 6+ years of experience in data science or applied machine learning roles.
  • Strong communication and stakeholder management skills.
  • Strong proficiency in Python (NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow or similar).
  • Advanced SQL skills for data manipulation and analysis.
  • Proven experience in anomaly detection techniques (statistical, ML-based, deep learning-based).
  • Strong understanding and practical application of causal inference and causal reasoning methodologies.
  • Hands-on experience with large-scale structured and time series datasets.
  • Solid knowledge of time series modeling (ARIMA, Prophet, LSTM, state-space models, etc.).
  • Experience deploying models into production environments.
  • Strong analytical thinking and problem-solving capabilities.

Preferred Qualifications
  • Experience in AIOps, IT Operations analytics, or observability platforms.
  • Exposure to GenAI / LLM-based solutions for operational intelligence.

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