Software Engineer

Overview of Role:  

As an ML Engineer, you will lead the development and integration of machine learning and AI based software solutions in collaboration with ML Scientists and our clients. Engineers are core contributors at Strong, with responsibilities such as building and managing application pipelines, embedding statistical algorithms in robust software applications, and deploying ML applications to the cloud. 

At Strong, a OneSix Company, your work will have an immediate impact on clients addressing real world challenges. Our work spans industries such as manufacturing, gaming, agriculture, automotive, and healthcare, among others. We pride ourselves not only in building the right solutions for our clients through research and development, but in implementing and scaling up those solutions through strong engineering. This role thus requires a deep expertise in applying data science and machine learning to real -world problems where data must be gathered, transformed, cleaned, and integrated into a larger architecture.

Responsibilities: 

• Work with machine learning scientists and other engineers to develop custom ML solutions for a
  variety of industries and use cases
• Build machine learning pipelines from ideation up through prototyping, development, and
  deployment
• Build data pipelines which can ingest, transform, and export data
• Embed statistical algorithms and machine learning models in cloud-based applications
• Help identify areas of opportunity and improvement within projects
• Collaborate in a fully remote, Agile-like environment using tools like Slack and Git (with a lot of
  uninterrupted development time)

Qualifications:     

• Minimum 2 years of industry experience writing and deploying Python-based
• Python
  
  • Very strong proficiency in Python.
  • Experience in packaging custom Python applications.
• API Development
  • Proficient in developing RESTful and gRPC APIs.
  • Experience using FastAPI.
• Big Data
  • Experience using PySpark.
• DevOps Skills
  • Containerization
    • Proficiency in containerizing applications using Docker and deploying on Kubernetes or cloud-based container services platforms (e.g., ECS).
  • Infrastructure as Code
    • Experience defining and deploying infrastructure-as-code with Terraform.Cloud Services
    • Hands-on experience with AWS services including EKS, ECS, EMR, Batch, Glue, Lambda, and SQS.
  • Data/ML Experience
    • Strong knowledge of SQL.
    • Experience integrating with ML models (locally or externally hosted).
    • Experience with Nvidia Triton, Azure, and Snowflake is ideal but not required.
• Demonstrated problem solving and quantitative skills, combined with excellent written and verbal communication skills are required

Last updated on Jul 26, 2024