Machine Learning Engineer

Experience

Minimum:

Proven experience in:

2+ yrs software development experience Strong analytical and problem-solving skills Expert in Python and SQL Experience with the modern software development best practices, e.g. agile software development code reviews unit testing version control, e.g. git CI/CD Experience with microservice architectures Experience working in an agile team Experience with ML frameworks and tools (e.g. pandas, numpy, scikit-learn, TensorFlow, Pytorch, Spark MLlib) Experience with modern ETL, compute and orchestration frameworks, e.g. Apache Spark, Apache Flink, Apache Kafka, etc.

Development experience in both Windows and Linux

Experience with container technologies, e.g. Docker, Kubernetes

Ideal:

Experience in building machine learning or AI systems

Proficiency in R language

• Experience deploying models to production
• Experience building distributed systems

Experience with NoSQL databases

Experience working with ML platforms, e.g. MLflow, Kubeflow, etc.

Experience working with Data Science platforms, e.g. Dataiku, Domino, etc.

Experience with cloud-based infrastructure, e.g. Azure, AWS, GCP; ideally AWS

Qualifications (Minimum)

A relevant qualification in Information Technology - Computer Science or Engineering

Qualifications (Ideal or Preferred)

• Masters Degree in Information Technology - Computer Science or Engineering - Other
• Knowledge

Min:

Must have knowledge of:

• Object oriented and functional programming in Python
• Modern software development practices
• Database querying using SQL
• Data life cycle
• Machine learning concepts
• Machine learning model life cycle
• Microservice architectures

Ideal:

Knowledge of:

Data Science lifecycle

• Distributed system design
• Big data storage and processing solutions
• Machine learning model architectures
• Skills

Analytical Skills

• Decision making skills
• Planning, organising and coordination skills
• Problem solving skills
• Researching skills
• Originally posted on Himalayas