Job Description:
Model Development & Deployment
Model fine-tuning: Use open-source libraries like DeepSpeed, Hugging Face Transformers, JAX, PyTorch, and TensorFlow to improve model performance Large Language Model Operations (LLMOps)
Model deployment and maintenance: deploying and managing LLMs on cloud platforms
Model training and fine-tuning: training and refining LLMs to improve their performance on specific tasks
work out how to scale LLMs up and down, do blue/green deployments and roll back bad releases
Data Management & Pipeline Operations
Curating and preparing training data, as well as monitoring and maintaining data quality
Data prep and prompt engineering: Iteratively transform, aggregate, and de-duplicate data, and make the data visible and shareable across data teams
Building vector databases to retrieve contextually relevant information
Monitoring & Evaluation
Monitoring and evaluation: tracking LLM performance, identifying errors, and optimizing models
Model monitoring with human feedback: Create model and data monitoring pipelines with alerts both for model drift and for malicious user behavior
Establish monitoring metrics
Infrastructure & DevOps
Continuous integration and delivery (CI/CD), where CI/CD pipelines automate the model development process and streamline testing and deployment
Develop and manage infrastructure for distributed model training (e.g., SageMaker, Ray, Kubernetes). Deploy ML models using containerization (Docker)
Required Technical Skills
Programming & Frameworks
Use open-source libraries like DeepSpeed, Hugging Face Transformers, JAX, PyTorch, and TensorFlow
LLM pipelines, built using tools like LangChain or LlamaIndex
Python programming expertise for ML model development
Experience with containerization technologies (Docker, Kubernetes)
Cloud Platforms & Infrastructure
Familiarity with cloud platforms like AWS, Azure, or GCP, including knowledge of services like EC2, S3, SageMaker, or Google Cloud ML Engine for scalable and efficient model deployment
Deploying large language models on Azure and AWS clouds or services such as Databricks
Experience with distributed training infrastructure
LLM-Specific Technologies
Vector databases for RAG implementations
Prompt engineering and template management
Techniques such as few-shot and chain-of-thought (CoT) prompting enhance the model's accuracy and response quality
Fine-tuning and model customization techniques
Knowlege Graphs
Relevance Engineering
Location:
DGS India - Pune - Baner M- AgileBrand:
MerkleTime Type:
Full timeContract Type:
Permanent