What are the responsibilities and job description for the AI/ML Engineer position at VeeRteq Solutions LLC?
Title: AI/ML engineer
AI/ML Engineer: strongest on SageMaker Bedrock
Duration: 14 Weeks
Location: Remote (US/Canada)
Note consultants should be able to travel with in US to Greenville site once every month.
Must-have: Strong SageMaker experience, strong Bedrock experience, solid understanding of ML frameworks, and the ability to build, deploy, test, and run machine learning models in SageMaker.
Nice-to-have: IoT exposure, Lookout for Equipment, Step Functions, and supporting data engineering knowledge.
Position Overview
We are seeking a Senior Solutions Architect with AI/ML expertise to lead the use case architecture design, ML pipeline development, and edge inference strategy. This role involves designing solution architectures for two priority industrial use cases and defining the MLOps pipeline for model development, training, and deployment.
Key Responsibilities
Technical Leadership
Design detailed solution architectures for two high-priority industrial use cases
Architect ML pipelines for predictive maintenance, anomaly detection, or quality inspection
Design edge inference capabilities for real-time decision support at the plant level
Define MLOps pipeline including model development, training, deployment, and monitoring
Conduct use case prioritization and requirements workshops
Customer Engagement
Lead use case detailed requirements workshops
Present AI/ML solution architectures to technical and business stakeholders
Collaborate with OT and data teams to identify high-value ML opportunities
Support use case backlog development leveraging the MIDA platform
Solution Development
Design feature engineering approaches for time-series and industrial data
Architect model monitoring, drift detection, and retraining workflows
Define containerized inference deployment for edge and cloud
Document ML solution designs and implementation roadmaps
Qualifications
Experience
7 years in machine learning, data science, or AI engineering
Experience with industrial or manufacturing ML use cases (predictive maintenance, quality, etc.)
Experience deploying models at the edge and in production environments
Technical Skills (AWS Services, Would Consider Competitive Alternatives)
Amazon SageMaker (Studio, Pipelines, Endpoints)
Amazon Bedrock (foundation models, agents)
AWS IoT Greengrass ML Inference
Amazon Lookout for Equipment / Lookout for Vision
AWS Step Functions (ML workflow orchestration)
Amazon ECR / ECS (containerized inference)
ML Frameworks: TensorFlow, PyTorch, Scikit-learn
MLOps: CI/CD for ML, model versioning, monitoring
Soft Skills
Strong communication skills for technical and business audiences
Ability to translate business problems into ML solutions
Experience working in agile environments
Customer-focused mindset
AWS Certifications (Nice to have)
AWS Certified Machine Learning - Specialty
AWS Certified Solutions Architect - Associate or Professional
AI/ML Engineer: strongest on SageMaker Bedrock
Duration: 14 Weeks
Location: Remote (US/Canada)
Note consultants should be able to travel with in US to Greenville site once every month.
Must-have: Strong SageMaker experience, strong Bedrock experience, solid understanding of ML frameworks, and the ability to build, deploy, test, and run machine learning models in SageMaker.
Nice-to-have: IoT exposure, Lookout for Equipment, Step Functions, and supporting data engineering knowledge.
Position Overview
We are seeking a Senior Solutions Architect with AI/ML expertise to lead the use case architecture design, ML pipeline development, and edge inference strategy. This role involves designing solution architectures for two priority industrial use cases and defining the MLOps pipeline for model development, training, and deployment.
Key Responsibilities
Technical Leadership
Design detailed solution architectures for two high-priority industrial use cases
Architect ML pipelines for predictive maintenance, anomaly detection, or quality inspection
Design edge inference capabilities for real-time decision support at the plant level
Define MLOps pipeline including model development, training, deployment, and monitoring
Conduct use case prioritization and requirements workshops
Customer Engagement
Lead use case detailed requirements workshops
Present AI/ML solution architectures to technical and business stakeholders
Collaborate with OT and data teams to identify high-value ML opportunities
Support use case backlog development leveraging the MIDA platform
Solution Development
Design feature engineering approaches for time-series and industrial data
Architect model monitoring, drift detection, and retraining workflows
Define containerized inference deployment for edge and cloud
Document ML solution designs and implementation roadmaps
Qualifications
Experience
7 years in machine learning, data science, or AI engineering
Experience with industrial or manufacturing ML use cases (predictive maintenance, quality, etc.)
Experience deploying models at the edge and in production environments
Technical Skills (AWS Services, Would Consider Competitive Alternatives)
Amazon SageMaker (Studio, Pipelines, Endpoints)
Amazon Bedrock (foundation models, agents)
AWS IoT Greengrass ML Inference
Amazon Lookout for Equipment / Lookout for Vision
AWS Step Functions (ML workflow orchestration)
Amazon ECR / ECS (containerized inference)
ML Frameworks: TensorFlow, PyTorch, Scikit-learn
MLOps: CI/CD for ML, model versioning, monitoring
Soft Skills
Strong communication skills for technical and business audiences
Ability to translate business problems into ML solutions
Experience working in agile environments
Customer-focused mindset
AWS Certifications (Nice to have)
AWS Certified Machine Learning - Specialty
AWS Certified Solutions Architect - Associate or Professional