Below is the Job Description for ML Engineer

V2 Solutions

We are looking for an ML Engineer to be part of the Data Science Platform team. They will be responsible for planning, designing, developing and deploying ML/data-powered solutions into production. An ideal candidate must have a strong developer background and experience with ML systems in production. Together with requirement gathering and solution design, they must have hands-on experience in developing large scale solutions in high throughput and low latency settings. The candidate must be able to clearly communicate requirements and comment on available choices and solutions. An innovative mindset and product thinking is an important quality we seek to find in the candidate.

Responsibilities: 

- Ideate on the solution deployment for a problem statement

- Design an end-state for a model in production and plan the stages of delivery

- Make offline models production-ready and deploy at scale

- Support the deployment of tools and frameworks for automation of ML training, inferencing, and monitoring workflows

- Institute and promote software development best practices such as CI/CD, automation, logging, security etc. into ML development

- Evangelise tooling and reusability of components to support data scientists in development best practices.

- Work with data engineers to design data pipelines and make data available for ML systems

- Work with Ops team to set up tools and frameworks for ML development and deployment

- Is available for L2 support of deployed ML systems.

Requirements / Skills:

- Professional experience building end-to-end software platforms

- Experience with designing, developing and maintaining ML systems

- Experience integrating data systems into applications

- Strong analytical and problem resolution skills

- Good understanding of ML development lifecycle

- Good understanding of statistical ML and deep learning frameworks. At least one of PyTorch, Tensorflow, or Keras. And scikit-learn.

- Highly proficient in Python and Java or Scala

- Understanding of Databases and Big Data technologies

- Good at data manipulation using pyspark and pandas.

- Experience with creating features pipelines

- Good understanding of containers

- Knowledge of orchestration platforms such as Airflow

- Experience with Agile development practices for planning and execution

- Good communication skills.

Skills:

ML development experience, Software development experience, Analytical mindset, Communication skill, Deep Learning, Design, monitoring, Mulesoft, Training, Java, Big Data, Outbound Voice Support, Inbound Voice Support, ETL Tools, Python, RPA, Scala, Data Science, Engineering, aws securities, aws devops

Experience: 5 to 12 Years.

Notice Period: 15 days