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Ml Inference Jobs in Arizona (NOW HIRING)

Sahi Softtech

ML Ops Engineer

Phoenix, AZ · On-site

... ML models, automate deployment workflows, and ensure models are reliable, scalable, secure, and ... Build scalable model serving solutions for batch, real-time, and event-driven inference use cases.

Cliftonlarsonallen

Lead ML Ops Engineer

Tempe, AZ

$98K - $129K/yr

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

Deloitte

Federated Systems Field Engineer

Gilbert, AZ · Hybrid

... ML or GenAI solutions, with exposure to model architecture, training, or inference workflows * 2+ years of experience developing in Python for data engineering, automation, or machine learning use ...

Deloitte

Federated Systems Field Engineer

Gilbert, AZ · On-site

... ML or GenAI solutions, with exposure to model architecture, training, or inference workflows * 2+ years of experience developing in Python for data engineering, automation, or machine learning use ...

Cisco

Applied AI Scientist

Phoenix, AZ

Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...

Inficare Technologies

Lead AI Engineer

Phoenix, AZ · On-site

$99K - $131K/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 ...

Cisco

Applied AI Scientist

Tempe, AZ

Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...

Deloitte

Google AI Lead Architect

Tempe, AZ · On-site

$53 - $72.50/hr

Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...

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 is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often involving advanced skills in deep learning, data modeling, and programming with tools like Python and TensorFlow. These positions usually require extensive experience, specialized knowledge, and may include leadership responsibilities or strategic decision-making.

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 engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning, and expertise in deploying large-scale models can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized knowledge and impact on product development.

Which 3 jobs will survive AI?

Jobs involving Ml Inference, such as data scientists, machine learning engineers, and AI system architects, are likely to persist as they require specialized expertise in developing, deploying, and maintaining AI models. These roles demand critical thinking, domain knowledge, and skills in programming and data analysis that are less easily automated. Continuous learning and staying updated with AI tools and frameworks are essential for these professions to remain relevant.

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.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and optimize AI models and systems. While AI automation tools can assist with certain tasks, MLEs are essential for building, tuning, and maintaining complex models, making complete replacement unlikely in the near term. Their expertise in data handling, model deployment, and system integration remains critical in AI development environments.

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.
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Ml Inference jobs near you
ML Ops Engineer

ML Ops Engineer

Sahi Softtech

Phoenix, AZ • On-site

Other

Posted 7 days ago

Job description

We are looking for an experienced MLOps Engineer to design, build, deploy, and maintain scalable machine learning operations pipelines on AWS. The ideal candidate will work closely with data scientists, machine learning engineers, data engineers, and DevOps teams to productionize AI/ML models, automate deployment workflows, and ensure models are reliable, scalable, secure, and well-monitored in production environments.

Key Responsibilities:

Design, build, and maintain end-to-end MLOps pipelines for model training, validation, deployment, monitoring, and retraining.

Develop and automate CI/CD pipelines for machine learning models and related services using tools such as AWS CodePipeline, AWS CodeBuild, Jenkins, GitLab CI/CD, or GitHub Actions.

Deploy, manage, and monitor machine learning models on AWS using services such as Amazon SageMaker, AWS Lambda, Amazon ECS, Amazon EKS, and API Gateway.

Build scalable model serving solutions for batch, real-time, and event-driven inference use cases.

Implement model versioning, experiment tracking, artifact management, and reproducibility using tools such as Amazon SageMaker Model Registry, MLflow, or similar platforms.

Containerize ML applications and services using Docker and deploy them using Kubernetes, Amazon EKS, or Amazon ECS.

Collaborate with data scientists and AI/ML engineers to move machine learning models from development to production.

Monitor production models for performance, accuracy, latency, data drift, model drift, and system reliability.

Build automation for model retraining, validation, approval workflows, and production deployment.

Work with AWS data and storage services such as Amazon S3, Amazon Redshift, AWS Glue, Amazon Athena, Amazon RDS, and DynamoDB as needed.

Implement infrastructure as code using Terraform, AWS CloudFormation, or AWS CDK.

Ensure security, access control, compliance, and governance using AWS IAM, VPC, CloudWatch, CloudTrail, KMS, and related AWS services.

Troubleshoot and resolve issues related to ML pipelines, cloud infrastructure, deployments, data pipelines, and production model performance.

Document MLOps processes, deployment standards, monitoring practices, and operational runbooks.

Required Skills and Qualifications:

Bachelor’s degree in Computer Science, 

Engineering, Data Science, Information Technology, or a related field.

Strong experience in MLOps, DevOps, machine learning engineering, cloud engineering, or platform engineering.

Hands-on experience with AWS cloud services, especially Amazon SageMaker, S3, Lambda, ECS, EKS, IAM, CloudWatch, and related services.

Strong programming experience with Python.

Experience with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, XGBoost, or similar.

Hands-on experience building CI/CD pipelines and automating deployment workflows.

Strong knowledge of Docker, containerization, and container orchestration using Kubernetes, Amazon EKS, or Amazon ECS.

Experience with model deployment patterns, including real-time inference, batch inference, and API-based model serving.

Familiarity with ML lifecycle tools such as SageMaker Pipelines, SageMaker Model Registry, MLflow, Kubeflow, or DVC.

Experience with infrastructure as code tools such as Terraform, CloudFormation, or AWS CDK.

Good understanding of model monitoring, data drift, model drift, logging, alerting, and production support.

Knowledge of version control tools such as Git.

Strong troubleshooting, analytical, communication, and collaboration skills.

Preferred Qualifications:

AWS certification such as AWS Certified Machine Learning – Specialty, AWS Certified Solutions Architect, or AWS Certified DevOps Engineer.

Experience with data engineering tools such as AWS Glue, Apache Spark, Airflow, Kafka, or Databricks.

Experience with feature stores, model registries, automated retraining pipelines, and model governance.

Understanding of security best practices for cloud-based ML environments.

Experience working in Agile/Scrum development environments.

Apply