Software Engineer, Machine Learning

Machine Learning

Location: Hybrid Model (3 days In month)

Duration: Long term

The Expertise You Have

• Has Bachelor's or Master's Degree in a technology related field (e.g. Engineering, Computer Science, etc.).
• 5+ years of experience in implementing Big data solutions in data analytics space.
• 1-2 years of experience in developing ML infrastructure and MLOps in the Cloud (AWS preferred).
• Experience in Object Oriented Programming (Java, Scala, Python), Unix scripting or related programming languages and exposure to some of Python's ML ecosystem (numpy, panda, sklearn, tensorflow, etc.).
• Experience with building data pipelines in getting the data required to build and evaluate ML models, using tools like Apache Spark or other distributed data processing frameworks.
• Data movement technologies (ETL/ELT), Messaging/Streaming Technologies (AWS SQS, Kinesis/Kafka), Relational and NoSQL databases (Cassandra, Elastic search, Graph database), API and in-memory technologies.
• Strong knowledge of developing highly scalable distributed systems using Open-source technologies.
• Experience with CI/CD tools (e.g., Jenkins or equivalent), version control (Git), orchestration/DAGs tools (Airflow, Luigi, Kubeflow, or equivalent).
• Solid experience in Agile methodologies (Kanban and SCRUM).
• Cloud experience (AWS preferred).

The Skills You Bring

• You have good technical design and analysis skills.
• You the ability to deal with ambiguity and work in fast paced environment.
• Your experience supporting mission critical applications.
• You are familiar with applied data science methods, feature engineering and machine learning algorithms.
• Your Data wrangling experience with structured and unstructured data.
• Your experience building ML infrastructure, with an eye towards software engineering.
• You have superb 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.

The Value You Deliver

• Examples of specific problems you'll help solve – build tools to help detect shifts in data/features used by ML models to help identify issues in advance of deteriorating prediction quality, monitoring the uncertainty of model outputs, automating prediction explanation for model diagnostics. Designing & developing a feature generation & store framework that promotes sharing of data among different ML models, etc.
• Designing, Building and supporting mission critical applications to provide the best customer experience
• Exploring new technology trends and leveraging them to simplify our data and ML ecosystem
• Driving Innovation and implementing solutions with future thinking
• Effectively partner with applied Data Scientists and lay software foundations upon which models can be built
• Guiding teams to improve development agility and productivity
• Resolving technical roadblocks to the team and mitigating potential risks