India: AI Engineer

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Skills you'll use

What you'll do

• Design, develop, and implement advanced machine learning models and algorithms to solve complex business problems.
• Leverage expertise in Python, Machine Learning to develop cutting-edge data-driven solutions.
• Utilize NLP and NLU techniques to extract insights, classify, and process information from unstructured text data in documents.
• Design, build, and deploy agentic AI and generative AI (GenAI) applications, including Retrieval-Augmented Generation (RAG) pipelines and LLM-powered agents, to automate workflows and enhance decision-making.
• Build and maintain full-stack applications — front-end interfaces and back-end services/APIs — that deliver AI/ML and GenAI capabilities to end users.
• Conduct data analysis, pre-processing, and modeling to extract valuable insights and drive data-driven decision-making.
• Collaborate with cross-functional teams to understand business requirements and translate them into actionable AI/ML solutions.
• Stay up to date with the latest advancements in AI/ML technologies, frameworks, and tools, and proactively learn and adopt new technologies to enhance our offerings.
• Communicate effectively with stakeholders, presenting findings, recommendations, and project progress in a clear and concise manner.

Who can apply

• Machine Learning (e.g., CNN, RNN, GAN, Transformers, Encoder-Decoder architectures, NLTK, Transformers).
• Knowledge and experience in Python.
• Full-Stack Software Development: Proficiency building complete web applications, including front-end (e.g., JavaScript/TypeScript, React, HTML/CSS) and back-end development (e.g., Python frameworks such as FastAPI, Flask, or Django; RESTful/GraphQL API design; SQL/NoSQL databases).
• Agentic AI & Generative AI: Hands-on experience designing, building, and deploying agentic AI systems and LLM-powered applications, including orchestration and agent frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel, LangGraph, AutoGen, CrewAI), vector databases (e.g., FAISS, Pinecone, Azure AI Search), and prompt engineering.
• Data Analysis and Modeling.
• AI/ML Architecture: Design and develop scalable and efficient AI/ML architectures to support the deployment of machine learning models.
• Data Engineering: Work closely with data engineers to ensure the availability, quality, and reliability of data required for AI/ML projects.
• RAG Pipelines: Implement robust and reliable RAG (Retrieval - Augmented Generation) pipelines to automate the end-to-end AI/ML workflow.
• MLOps: Implement MLOps practices to streamline the development, deployment, and management of AI/ML models.
• Azure AI Tools: Experience with Azure AI tools and services, such as Azure Machine Learning, Azure Databricks, and Azure Cognitive Services.

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