Machine Learning Solutions Engineer

AssetWatch®; Columbus (OH); Remote; USD 115,000 - 185,000; Full time

Job summary

A leading company in predictive maintenance is seeking a Machine Learning Solutions Engineer to develop and manage ML models on AWS. This role involves collaborating with teams to implement ML pipelines, ensuring model performance, and mentoring engineers. The ideal candidate will have extensive experience in ML, AWS, and programming, contributing to innovative solutions in manufacturing.

Benefits

Competitive compensation package including stock options

Flexible work schedule

Competitive benefits and 401k match

Unlimited PTO

Qualifications

• At least 5 years of industrial experience in ML and with AWS.
• Demonstrable experience in deploying and prototyping AI models on AWS.
• Strong proficiency in programming languages such as Python and SQL.

Responsibilities

• Set up optimal ML infrastructure on AWS and construct a robust pipeline.
• Ensure efficient data ingestion mechanisms and design Feature Store Data Models.
• Seamlessly transition trained models into AWS production environments.

Skills

Machine Learning

AWS

Python

SQL

Education

BS or MS in Computer Science, Computer Engineering, or related field

Tools

Amazon SageMaker

ECS

Lambda

Job description

1 day ago Be among the first 25 applicants

AssetWatch serves global manufacturers by powering manufacturing uptime through the delivery of an unparalleled condition monitoring experience, with a passion to care about the assets our customers care for every day. We are a devoted and capable team that includes world-renowned engineers and distinguished business leaders united by a common goal – To build the future of predictive maintenance. As we enter the next phase of rapid growth, we are seeking people to help lead the journey.

We're seeking an experienced Machine Learning Solutions Engineer to join our team and play a critical role in developing, deploying, and managing machine learning models on AWS. You will collaborate with cross-functional teams to implement end-to-end ML pipelines, ensure seamless model deployment, and continuously monitor and evaluate model performance. As a subject matter expert in MLOps, you will also mentor other engineers and contribute to the development of our company's ML engineering capabilities.

What You'll Do:

• Infrastructure & Pipeline Management: Set up optimal ML infrastructure on AWS and construct a robust pipeline, covering data preprocessing, model training, and tuning.
• Data Ingestion & Feature Management: Ensure efficient data ingestion mechanisms and design Feature Store Data Models for streamlined storage of engineered features.
• AI Model Deployment on AWS: Seamlessly transition trained models into AWS production environments, ensuring integration and performance.
• Automation & Scaling: Streamline the model training process with automation, ensuring scalability and adaptability.
• Inference Pipelines: Craft inference mechanisms post-training that prioritize client load balancing and utilize containerization.
• Security & Version Control: Implement top-tier security standards, especially during deployment, and maintain best practices for ML model and data versioning.
• Model Monitoring & Evaluation: Establish mechanisms for data drift or concept drift post-deployment and initiate A/B tests to guide model refinements.
• Team Engagement & Continuous Learning: Collaborate with interdisciplinary teams throughout the model's lifecycle and stay updated on MLOps trends, AWS, and machine learning innovations.
  
  

Who You Are:

• BS or MS in Computer Science, Computer Engineering, or related field.
• At least 5 years of industrial experience in ML and with AWS.
• Demonstrable experience in deploying and prototyping AI models on AWS.
• Hands-on experience with specific AWS compute tools including AWS/Amazon SageMaker, container and deployment in ECS, Lambda, etc.
• Proficiency with various purpose-built databases within AWS, particularly those optimized for ML workloads.
• Strong proficiency in programming languages such as Python and SQL.
• Deep understanding of containerization techniques, especially in the context of ML model inference.
  
  

Bonus Points For:

• Familiarity with LLMs, Vector Databases and integration tools such as LangChain
• Understanding of a deep learning framework such as TensorFlow/Keras, or PyTorch and HuggingFace for LLMs
• Familiarity with AI Model Explainability and Interpretability techniques
• Experience with cloud-based services for IIoT
• Familiarity with condition monitoring and predictive maintenance methodologies.
• Experience with big data processing frameworks like Apache Spark or Hadoop.
• AWS Certification(s)
• Familiarity with Infrastructure-as-Code (IaC) tools like AWS CDK or Terraform
  
  

What We Offer:

AssetWatch is a remote-first rapidly growing startup providing a game changing condition monitoring platform and mobile experience in the industrial manufacturing space.

• Competitive compensation package including stock options
• Flexible work schedule
• Competitive benefits and 401k match
• Opportunity to make a real impact every day
• Work with a passionate and growing team
• Unlimited PTO
  
  

We have a distributed team that works remotely across locations in the United States. We are open to candidates from most states but collaboration within core working hours is required.

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Automation Machinery Manufacturing

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