Python-GenAI-LLMOps
Infosys Bengaluru East, Karnataka, India
Job Description
"Unlock the power of Generative AI and Large Language Models to revolutionize applications with Python-GenAI-LLMOps at Infosys."
In today's data-driven world, Generative AI and Large Language Models (LLMs) are transforming industries with their ability to generate human-like text, images, and other content. As a Python-GenAI-LLMOps professional at Infosys, you'll be at the forefront of this revolution, developing and deploying cutting-edge AI applications that drive business growth and innovation.
With a strong foundation in Python, Machine Learning, and Spatial Databases, you'll work on designing and implementing end-to-end AI/ML workflows, building and maintaining LLM pipelines, and integrating LLMs into applications via APIs and microservices. Your expertise will be sought after by data engineers, product teams, and stakeholders to drive AI-powered solutions.
Why you should learn this:
High demand for Python-GenAI-LLMOps professionals in the industry, with a projected growth rate of 30% over the next 5 years.
Expected Salary: Competitive salary range of $120,000 - $180,000 per annum, depending on experience and location.
How it works:
- Step 1: Develop and deploy Generative AI applications using LLMs (GPT, Llama, etc.)
- Step 2: Build and maintain LLM pipelines, including prompt engineering and fine-tuning
- Step 3: Design and implement end-to-end AI/ML workflows (data → model → deployment)
- Step 4: Work with vector databases for semantic search and retrieval (RAG architecture)
- Step 5: Implement LLMOps practices for monitoring, evaluation, and versioning
Core Concepts to Master
Large Language Models (LLMs)
LLMs are a type of deep learning model that can process and generate human-like text, images, and other content. They are trained on massive datasets and can be fine-tuned for specific tasks, such as language translation, text summarization, and question-answering.
Prompt Engineering
Prompt engineering is the process of designing and optimizing input prompts to elicit the desired response from an LLM. This involves understanding the LLM's capabilities, limitations, and bias, as well as the context and requirements of the task at hand.
LLMOps
LLMOps is a set of practices and tools for monitoring, evaluating, and versioning LLMs. This includes tracking model performance, identifying areas for improvement, and ensuring that models are properly versioned and deployed.
Vector Databases
Vector databases are specialized databases that store and index vector representations of data, such as text, images, and audio. They enable fast and efficient search and retrieval of data, and are commonly used in applications such as recommendation systems and semantic search.
Interview Questions (Beginner)
- What is a Large Language Model (LLM), and how is it different from a traditional machine learning model?
- Can you explain the concept of prompt engineering, and how is it used in LLM applications?
- What is LLMOps, and why is it important for developing and deploying LLMs?
Job Overview
Advance Questions
- • How do you design and implement end-to-end AI/ML workflows, including data preparation, model training, and deployment?
- • Can you explain the concept of vector databases, and how are they used in semantic search and retrieval?
- • How do you optimize the performance, cost, and latency of LLMs in production environments?