AI/ML Ops Engineer with AWS experience

Qualifications

• Deep SageMaker MLOps platform design
• Experience building feature stores
• Strong Step Functions / EventBridge automation
• AWS experience
• Python Experience
• 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)
• 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

Responsibilities

• Designing & developing a feature generation & store framework that promotes sharing of data/features among different ML models
• Partner with Data Scientists and to help use the foundational platform upon which models can be built and trained
• Operationalize ML Models at scale (e.g
• Serve predictions on tens of millions of customers)
• 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
• Exploring new technology trends and leveraging them to simplify our data and ML ecosystem
• Driving Innovation and implementing solutions with future thinking
• Guiding teams to improve development agility and productivity
• Resolving technical roadblocks and mitigating potential risks
• Delivering system automation by setting up continuous integration/continuous delivery pipelines

Full Description

Job Title: AI/ML Ops Engineer with AWS experience - W2 Only - We can provide sponsorship as well

Duration: Long Term

Location: Durham, NC/Westlake, TX - Hybrid

Must Have:

• Deep SageMaker MLOps platform design

• Experience building feature stores

• Strong Step Functions / EventBridge automation

• AWS experience

• Python Experience

The Expertise You Have

• Has Bachelor’s or Master’s Degree in atechnology 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.

The Value You Deliver

• Designing & developing a feature generation & store framework that promotes sharing of data/features among different ML models.

• Partner with Data Scientists and to help use the foundational platform upon which models can be built and trained.

• Operationalize ML Models at scale (e.g. Serve predictions on tens of millions of customers).

• 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.

• Exploring new technology trends and leveraging them to simplify our data and ML ecosystem.

• Driving Innovation and implementing solutions with future thinking.

• Guiding teams to improve development agility and productivity.

• Resolving technical roadblocks and mitigating potential risks.

• Delivering system automation by setting up continuous integration/continuous delivery pipelines.