Job Description

Qualifications

5+ years of experience delivering machine learning, AI, and analytics solutions.
Graduate in Computer Science, IT, AI/ML, Data Science, E&T or related field.
Experience in automotive and manufacturing domain (preferred).
Excellent stakeholder management and communication skills.

Key Tech Stack

Cloud, AI & ML Platforms:

AWS (SageMaker, Bedrock, S3, ECS, Glue, Lambda, Step Functions, CloudWatch, CloudTrail)

Artificial Intelligence & Generative AI

LLMs and multimodal models using Amazon Bedrock, SageMaker JumpStart, or custom NLP models
Prompt engineering, retrieval-augmented generation (RAG), embeddings, vector databases (OpenSearch/OpenSearch Serverless/PG Vector)

Machine Learning

Regression, Classification, Time-Series Forecasting, Deep Learning (TensorFlow / PyTorch), NLP, Computer Vision, Feature Engineering, Model Monitoring, SageMaker Pipelines & Model Registry

Programming

Python (mandatory), SQL

Version Control & CI/CD

GitHub, GitLab, AWS CodeCommit/AWS CodePipeline

Security & Monitoring

IAM, VPC security, key management (KMS), logging and model auditability using CloudWatch, SageMaker Model Monitor and Bedrock Guardrails

Other

Cost optimization, performance tuning, responsible AI practices, MLOps frameworks.

Key Responsibilities

Collaborate with business and IT teams to translate AI, ML, and analytical requirements into scalable solutions.
Build, train, fine-tune, and deploy ML and Generative AI models using AWS SageMaker and Amazon Bedrock.
Develop automated feature engineering and data pipelines to support ML and AI workloads on AWS.
Lead insights generation, data storytelling, and operational analytics using QuickSight or equivalent BI tools.
Implement responsible AI principles including model explainability, safety, fairness, and governance.
Define and enforce standards for ML lifecycle management, security, and compliance (GDPR, ISO, etc.).
Mentor and support teams including ML engineers, data engineers, and analytics developers.
Drive MLOps and GenAIOps adoption including CI/CD, automation, model monitoring, and operational excellence.
Evaluate emerging AWS GenAI capabilities and continuously adopt best practices (peer reviews, reusable templates, documentation).

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Senior Engineer, Data Scientist - Nissan ISIT

Renault Nissan Technology & Business Centre India

Job Summary

Job Description

Interview Questions of Senior Engineer, Data Scientist - Nissan ISIT at Renault Nissan Technology & Business Centre India

Salary Insights of Senior Engineer, Data Scientist - Nissan ISIT at Renault Nissan Technology & Business Centre India