Associate Data Scientist

Birlasoft Inc
United States
2 months ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Starter
Experience required
5 years minimum
Working hours
Regular working hours
Job source

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence BigQuery Cloud Computing Security Cloud Storage Data Cleansing Data Governance Data Flow Control Identity and Access Management Machine Learning Performance Tuning Systems Development Life Cycle
+5 more
Google Cloud Containerization Data Lineage Google Cloud Functions Machine Learning Operations

Job description

Work on end-to-end development of GenAI/ML models: problem framing, data preparation, model selection, training, evaluation, and iteration.

Implement microservice-based AI solutions and deploy them in containerized environments (preferably GKE); define APIs and data contracts.

Leverage GCP offerings (Vertex AI, BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Run, GKE, etc.) to develop scalable AI solutions and efficient data workflows.

Deploy, monitor, and maintain models in production; implement observability (logs, metrics, tracing), cost optimization, and performance tuning.

Ensure cloud security, data governance, and compliance in line with regulatory requirements; manage IAM roles, data access controls, and data lineage.

Collaborate with cross-functional teams (data engineers, software engineers, product, regulatory/compliance, analytics) to translate business needs into robust ML solutions.

Stay current with GenAI advancements and evaluate new tools/approaches; produce reproducible experiments and artifacts.

Requirements

Do you have experience in Model deployment?, Summary: We are seeking a Junior Data Scientist with at least 5 years of hands-on experience in developing GenAI/machine learning models and deploying them in a cloud environment, preferably on Google Cloud Platform (GCP). The ideal candidate will develop microservice-based solutions, containerize deployments (e.g., GKE), and drive end-to-end SDLC practices. Experience in the pharma domain is a strong advantage.

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