Engr III Spec – AI/ML Engineering

Verizon

. Architect, implement, and maintain scalable infrastructure to train, fine-tune, and deploy state-of-the-art AI models .

Posted 9/15/2026full-timeHyderabad • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and maintaining scalable AI infrastructure, with a strong focus on deploying and optimizing ML models in production environments. Proficient in Python and modern ML frameworks, with experience in system design and MLOps practices.

Highest-signal resume keywords

Python ProgrammingMachine Learning FrameworksDistributed Model TrainingMLOps ToolsSystem Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel DeploymentQuantizationPruningDistillationData StructuresObject-Oriented ProgrammingPerformance OptimizationAlgorithm DevelopmentCode Review

Soft Skills

CollaborationMentoringTechnical Best Practices

Tools & Technologies

PyTorchTensorFlowJAXAWSGCPAzureDockerKubernetesRayDeepSpeed

Industry Keywords

AI ModelsML InfrastructureData PipelinesAutomated EvaluationModel InferenceProduction SystemsScalable Applications

Tech Stack

Tools & technologies

AWSAzureDockerGoogle Cloud PlatformKubernetesPythonPyTorchRayTensorflow

About the role

Key responsibilities & impact

Architect, implement, and maintain scalable infrastructure to train, fine-tune, and deploy state-of-the-art AI models
Collaborate with Research Scientists to translate theoretical concepts and prototypes into clean, modular, performant codebases
Optimize model inference and training workflows for memory, speed, and cost efficiency using quantization, pruning, distillation, and distributed execution
Design resilient data pipelines, dataset curation workflows, and automated evaluation suites
Build monitoring, logging, and continuous deployment systems for ML to track model performance, drift, and production system health
Conduct code reviews, mentor junior engineers, and drive technical best practices within the AI Science organization
Collaborate with Data Engineers and Platform Engineers on advanced ML models and AI infrastructure
Turn complex algorithmic innovations into robust, scalable, low-latency production applications

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related quantitative field, or four or more years of work experience
Three or more years focused on building, deploying, and maintaining ML/AI models in production
Expert-level programming proficiency in Python
Deep understanding of modern ML frameworks such as PyTorch, TensorFlow, or JAX
Strong background in system design, object-oriented programming, data structures, and software engineering best practices
Experience with distributed model training and inference systems such as Ray, DeepSpeed, vLLM, or Megatron-LM
Experience with AWS, GCP, or Azure
Experience with Docker, Kubernetes, and modern MLOps tool chains
Master’s degree in a related field is an additional preferred qualification

Benefits

Comp & perks

Full-time employment
Hybrid work arrangement with work-from-home and assigned office days