Data Scientist AWS & Google Cloud Platform | Auto Industry

Job Location: Atlanta, GA

Duration: Long Term

Job Type: W2 / Full time

Interview Mode: 2 Rounds (Tech Screening)

Local to Atlanta

Job Description:

We are seeking a Data Scientist with multi-cloud expertise (AWS & Google Cloud Platform) to build and optimize AI/ML solutions for the Auto and Auto Parts Manufacturing industry. This role requires hands-on experience in full ML lifecycle management and the ability to design scalable, industrial-grade ML pipelines using services across both AWS and Google Cloud Platform.

Key Responsibilities:

• Develop and deploy ML models for predictive maintenance, quality defect detection, process optimization, and parts lifecycle prediction.
• Utilize AWS SageMaker, Google Cloud Platform Vertex AI, and other cloud-native ML services for training and deployment.
• Integrate ML models with edge devices and automotive systems (e.g., MES, ERP) and manage real-time data ingestion using AWS Kinesis and Google Cloud Platform Pub/Sub.
• Prepare and clean IoT, sensor, and operations data using AWS Glue, Google Cloud Platform Dataflow, or BigQuery.
• Implement Generative AI use cases such as smart manuals, production note summarization, and knowledge retrieval using LLMs.
• Build robust CI/CD pipelines for ML (MLOps) using Vertex AI Pipelines, SageMaker Pipelines, and GitOps principles.
• Collaborate with plant engineers and data teams to align ML outputs with shop floor goals and business KPIs.

Required Skills:

• 4 7 years of experience with Machine Learning engineering in production environments.
• Expertise with AWS ML Stack (SageMaker, Glue, Lambda, S3) and Google Cloud Platform Stack (Vertex AI, BigQuery, Dataflow).
• Strong background in Python, data wrangling (Pandas, NumPy), and model development using TensorFlow or PyTorch.
• Understanding of automotive manufacturing data (shop floor, sensors, ERP) and related ML use cases.
• Hands-on experience with Generative AI tools and LLMs for industrial documentation or analytics.
• Proven skills in model monitoring, versioning, and lifecycle management in multi-cloud environments.

Preferred Experience:

• Experience with Auto OEMs or Tier-1/2 Auto Parts suppliers.
• Familiarity with industrial edge computing, digital twins, or AR/VR based defect tracking.
• AWS/Google Cloud Platform certifications related to ML or data engineering are a plus.

Note: This opportunity is with ITTStar. If interested or if you know someone who is a good fit, please apply or refer.