MLOps/DataOps Engineer

Position: MLOps, DataOps Engineer

BASIC QUALIFICATIONS

• Bachelor's or Masters degree in Computer Science, IT or related technical field
• 3+ years experience with programming languages such as Python and open-source technologies (PySpark, PyTorch, TensorFlow, LangChain, Transformers)
• 3+ years of experience with Azure data services like (Azure Synapse Analytics, Databricks, SQL Databases, Azure Machine Learning, Azure Cognitive Services etc.)

PREFERRED QUALIFICATIONS

• 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

Key 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 LangChains 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.