IN_Senior Associate _ ML Ops/Data Ops_ Emerging Technologies_ Advisory_ Gurugram

Responsibilities: 

• Creation and implementation of enterprise grade architectures for deployments of On-premises ML/LLM models for scalability and efficiency 

• Working with SEO team for SEO pages generation using LLMs with reduction in AI scores and humanizing the AI generated content 

• Fine-tuning LLM models and implementation of RAGs  

• Interacting with multiple teams and understanding the new requirements, doing PoC on newly proposed requirements proposing various solutions for optimization, efficiency and cost saving 

• Creation of Web Pages (Blogs) using regularly updated LLMs, with using NLP techniques for reduction of AI scores and improvement of SEO rankings 

• Implementation of Quality assurance checks for the content being generated from LLMs 

• Implementation of Lang Chains for achieving requirement specific use-cases 

• Setting up automated ML pipelines on Azure ML Studio for scheduled training of ML models, with CI/CD setups for automating deployments of trained ML model pickle/joblib dumps on corresponding infra like K8s, Linux Servers using tools like Jenkins and version control tools like Git 

• Experience on working with Frontend teams for image optimizations for improving the time and efficiency for rendering the web pages 

• Writing shell scripting for monitoring the efficiency of the generated outputs of LLM models and providing corresponding estimates of required resources for scalability requirements 

Mandatory skill sets: 

Python and open-source technologies (PySpark, PyTorch, TensorFlow, LangChain, Transformers) Azure data services like (Azure Synapse Analytics, Databricks, SQL Databases, Azure Machine Learning, Azure Cognitive Services etc.) 

Preferred skill sets: 

• Proficiency in Python and Azure, AWS DevOps & Data services. 

• Proficiency in AI/ML model operations, and experience fine-tuning models and RAG implementation 

• Experience in creation and implementation of Enterprise grade architectures for implementation and usage of Azure AI Services ensuring security, efficiency and scalability 

• Experience in building components for data scientists and assisting them in all stages of Model development and deployment.  

• Experience in Azure ML Studio for setting up pipelines for Model training and end point creation 

• Experience with IDE/notebook software (Jupyter Studio, VSCode, PyCharm, etc) 

• Experience in data extraction and analysis using Google Analytics, Azure Synapse and Big Query 

• Proficiency in Looker Studio for dynamic dashboards creation and funnel drops analysis 

• Writing Dockerfile, Shell Scripting and Kubernetes Manifests for deployment of customized applications on Servers and Kubernetes clusters 

Years of experience required: 

3 to 8 years 

Education qualification: 

BE/B.tech