Python Data Science Jobs in Richardson, TX (NOW HIRING)

Job Summary:
Keylent Inc is seeking an AI / ML Engineer to join their team. The role involves designing, building, and operating production-grade client applications, with a focus on data science, machine learning, and MLOps.
Responsibilities:
• At least 7 years of experience as a Client Engineer, with fluency and experience in Python, Data Science, Data Engineering & MLOPS
• Knowledge in RESTful API design and implementation
• Experience of Web framework like FastAPI/Tornado/Flask etc.
• Data Science knowledge and familiarity with Client libraries such as Pandas, Scikit, TensorFlow, xgboost, time series frameworks like prophet/or equivalent frameworks
• In-depth of big data frameworks like Pyspark & gcp tools like data proc, data proc serverless
• Experience designing, building and operating productions grade Client applications
• Knowledge of design patterns and architecture, data science, and machine learning best practices
• Experience with relational databases like Big Query, cloud environments, and a good understanding of optimizing storage cost/query cost while designing data engineering workflows
• Good knowledge of Kubernetes, container technologies, docker registries, and applying them in the context of machine learning systems
• Working knowledge of Client frameworks, such as Vertex, Kubeflow, MLflow, CloudRun etc.
• Experience in designing post-deployment model management framework e.g. model monitoring tools, workflows for feature drift, error analysis of models
• Proficiency with CI/CD tools, especially Jenkins
• Experience in designing CI/CD pipelines (Jenkins) for deployment of Data Engineering and Client jobs workflow
• Hands-on experience in orchestration frameworks like Airflow, Cloud Composer, DataProc Serverless for Pyspark jobs etc.
• In-depth understanding of Google Cloud ecosystem for Data Engineering & MLOps - cloud composer, dataproc, dataproc serverless, big query, cloudrun, vertex, vertex pipelines, GKE
• Experience in designing MLOps platforms and architect big data systems on GCP cloud
Qualifications:
Required:
• At least 7 years of experience as a Client Engineer, with fluency and experience in Python, Data Science, Data Engineering & MLOPS
• Knowledge in RESTful API design and implementation
• Experience of Web framework like FastAPI/Tornado/Flask etc.
• Data Science knowledge and familiarity with Client libraries such as Pandas, Scikit, TensorFlow, xgboost, time series frameworks like prophet/or equivalent frameworks
• In-depth of big data frameworks like Pyspark & gcp tools like data proc, data proc serverless
• Experience designing, building and operating productions grade Client applications
• Knowledge of design patterns and architecture, data science, and machine learning best practices
• Experience with relational databases like Big Query, cloud environments, and a good understanding of optimizing storage cost/query cost while designing data engineering workflows
• Good knowledge of Kubernetes, container technologies, docker registries, and applying them in the context of machine learning systems
• Working knowledge of Client frameworks, such as Vertex, Kubeflow, MLflow, CloudRun etc.
• Experience in designing post-deployment model management framework e.g. model monitoring tools, workflows for feature drift, error analysis of models
• Proficiency with CI/CD tools, especially Jenkins
• Experience in designing CI/CD pipelines (Jenkins) for deployment of Data Engineering and Client jobs workflow
• Hands-on experience in orchestration frameworks like Airflow, Cloud Composer, DataProc Serverless for Pyspark jobs etc.
• In-depth understanding of Google Cloud ecosystem for Data Engineering & MLOps - cloud composer, dataproc, dataproc serverless, big query, cloudrun, vertex, vertex pipelines, GKE
• Experience in designing MLOps platforms and architect big data systems on GCP cloud
Preferred:
• Good to have :- GCP
Company:
Keylent provides Software Development Services in the Cloud, Analytics and Digital Transformation space. Founded in 2010, the company is headquartered in Wilmington, USA, with a team of 201-500 employees. The company is currently Growth Stage.