Stefanini Group
Machine Learning Engineer
Dearborn, Michigan, USA · Contract
Sponsorship not specifiedDetected 99 days ago
PythonAlgorithmsGitSQLBigQuerySnowflakeRedshiftVector DatabasesGCPCloud PlatformsDockerKubernetesCI/CDRESTgRPCOAuthMachine LearningDeep LearningSparkAirflowData EngineeringNLPComputer VisionLLMs
About the role
- For example, designing and implementing a cloud-native application architecture using GKE (Google Kubernetes Engine) with Cloud SQL and Pub/Sub.
- Data Warehousing - Experience designing and maintaining data warehouse solutions (e.g., BigQuery, Snowflake, Redshift).
Responsibilities
- They automate and optimize the end-to-end ML and Gen AI model lifecycle using expertise in experimental methodologies, statistics, prompt engineering, and coding for tool building and analysis.
- For example, building ETL pipelines that process terabytes of daily event data and transform it into downstream analytics.
- For example, building a classification model using Vertex AI to predict customer churn, or implementing a rule engine that automates underwriting decisions.
- API - Experience designing, building, and consuming RESTful or gRPC APIs.
- For example, developing a versioned REST API with OAuth 2.0 authentication that serves as the integration layer between a mobile application and backend microservices.
- Experience developing robust APIs, preferably with FastAPI.
- Also, some positions may include bonuses or other incentives*** Stefanini takes pride in hiring top talent and developing relationships with our future employees.
Requirements
- Proficiency in Python programming.
- Solid experience with SQL for data manipulation and querying.
- Hands-on experience with Google Cloud Platform (Google Cloud Platform) services relevant to AI/ML.
- Experience with the OpenAI SDK.
- Proficiency with **version control systems (e.g., Git).
Skills
- Big Data - Experience working with large-scale data processing frameworks such as Apache Spark, Dataflow, or BigQuery.
Compensation
- For example, using Cloud Composer to orchestrate scheduled data pipelines that feed into a BigQuery data warehouse. **Listed salary ranges may vary based on experience, qualifications, and local market.
Benefits
- Basic understanding and practical experience with Machine Learning model fine-tuning.
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