NEW ROLE
Cloud Engineer (Machine Learning)
Hybrid scheduled
What are the top MUST have skills
1. ML Cloud Engineer
2. Python development (not scripting) experience
3. Comfortable with SQL
4. Experience with AWS cloud environment
5. Sagemaker
Cloud Engineer
As a Cloud Engineer, build and maintain large scale ML Infrastructure and ML pipelines. Contribute to building advanced analytics, machine learning platform and tools to enable both prediction and optimization of models. Extend existing ML Platform and frameworks for scaling model training & deployment. Partner closely with various business & engineering teams to drive the adoption, integration of model outputs. This role is a critical element to using the power of Data Science in delivering Clients promise of creating the best customer experiences in financial services.
The Expertise You Have
- Has Bachelor’s or Master’s Degree in a technology related field (e.g. Engineering, Computer Science, etc.).
- Experience in Object Oriented Programming (Java, Scala, Python), SQL, Unix scripting or related programming languages and exposure to some of Python’s ML ecosystem (numpy, panda, sklearn, tensorflow, etc.).
- Experience in building cloud native applications using AWS services like S3, RDS, CFT, SNS, SQS, Step functions, Event Bridge, cloud watch etc.,
- Experience with building data pipelines in getting the data required to build, deploy and evaluate ML models, using tools like Apache Spark, AWS Glue or other distributed data processing frameworks.
- Data movement technologies (ETL/ELT), Messaging/Streaming Technologies (AWS SQS, Kinesis/Kafka), Relational and NoSQL databases (DynamoDB, EKS, Graph database), API and in-memory technologies.
- Strong knowledge of developing highly scalable distributed systems using Open-source technologies.
- 5+ years of proven experience in implementing Big data solutions in data analytics space.
- Experience in developing ML infrastructure and MLOps in the Cloud using AWS Sagemaker.
- Extensive experience working with machine learning models with respect to deployment, inference, tuning, and measurement required.
- Experience with CI/CD tools (e.g., Jenkins or equivalent), version control (Git), orchestration/DAGs tools (AWS Step Functions, Airflow, Luigi, Kubeflow, or equivalent).
- Solid experience in Agile methodologies (Kanban and SCRUM).
The Skills You Bring
- You have strong technical design and analysis skills.
- You the ability to deal with ambiguity and work in fast paced environment.
- Your experience supporting critical applications.
- You are familiar with applied data science methods, feature engineering and machine learning algorithms.
- Your Data wrangling experience with structured, semi-structure and unstructured data.
- Your experience building ML infrastructure, with an eye towards software engineering.
- You have excellent communication skills, both through written and verbal channels.
- You have excellent collaboration skills to work with multiple teams in the organization.
- Your ability to understand and adapt to changing business priorities and technology advancements in Big data and Data Science ecosystem.