Data Scientist(Mlops+python) (NCS/Job/ 2011)

For A French Mnc It Company

3 - 6 Years

Full Time

Up to 30 Days

Up to 14 LPA

1 Position(s)

Bangalore / Bengaluru

Posted 11 Days Ago

Job Skills

Job Responsibilities

Design, build, and deploy Generative AI models using foundational models like GPT, BERT, LLaMA, PaLM, etc.
Develop scalable GenAI applications and integrate with enterprise systems using APIs and SDKs.
Fine-tune and optimize large language models (LLMs) for domain-specific use cases.
Design, implement, and manage end-to-end machine learning pipelines on Microsoft Azure, leveraging services like Azure Machine Learning, Azure DevOps, and Kubernetes.
Collaborate with data scientists to productionize ML models using best practices in Azure MLOps.
Automate the deployment and monitoring of models using CI/CD pipelines and Azure DevOps tools.
Implement scalable model training, validation, and deployment workflows in the cloud.
Monitor model performance in production and retrain models as needed to maintain accuracy and reliability.
Ensure security, compliance, and governance of ML workflows and data.
Develop Python scripts and tools to automate repetitive tasks and improve operational efficiency.
Troubleshoot and optimize ML workflows for performance and cost-effectiveness.
Document architecture, processes, and operational procedures.

Qualification

Hands-on experience with transformer-based models (e.g., GPT, BERT, LLaMA, etc.)
Familiarity with tools like LangChain, LlamaIndex, Haystack, etc.
Experience in prompt engineering, retrieval-augmented generation (RAG), and model fine-tuning.
Proven experience in MLOps, specifically with Azure services.
Strong programming skills in Python
Experience with Hugging Face Transformers, PyTorch or TensorFlow.
REST APIs and/or gRPC for model integration.
Experience with Azure Databricks, Azure Machine Learning, Azure OpenAI
Familiarity with ML libraries (scikit-learn, TensorFlow, PyTorch).
Experience building and managing CI/CD pipelines for ML models using Azure DevOps or equivalent tools.
Building REST APIs for ML inference using frameworks like FastAPI or Flask.
Understanding of containerization technologies like Docker and orchestration using Kubernetes.
Knowledge of machine learning lifecycle management, model versioning, and deployment strategies.
Experience with data engineering, data pipelines, and ETL processes on Azure.
Familiarity with monitoring tools and logging frameworks for production systems.
Strong problem-solving skills and ability to work in a collaborative, fast-paced environment.

Technical Skills

GenAI Models: GPT, BERT, LLaMA, PaLM
Framework: PyTorch or TensorFlow
Cloud Platforms: Microsoft Azure (Azure ML, Azure Databricks, AKS, Azure DevOps)
Programming Languages: Python
CI/CD Tools: Azure DevOps, GitHub Actions, Jenkins
Containerization: Docker, Kubernetes
Data Storage & Processing: Azure Blob Storage, Azure SQL, Azure Data Factory
Monitoring & Logging: Azure Monitor, Application Insights, Prometheus, Grafana

Mandatory Skills