Job Summary

This role is responsible for architecting, designing, and delivering end-to-end Generative AI solutions aligned with enterprise business objectives and technology strategy.

The position focuses on building scalable, secure, and production ready GenAI architectures using platforms such as Azure OpenAI, Hugging Face, and Databricks, and seamlessly integrating Generative AI capabilities into enterprise applications, data platforms, APIs, and business workflows. The role also involves designing and orchestrating multiagent architectures to enable complex, autonomous, end-to-end AI-driven workflows that deliver measurable business impact.

Responsibilities

- Architect, containerize, and deploy LLM applications in cloud environments using Docker, Podman, and Kubernetes following enterprise MLOps standards.
- Apply deep expertise in LLMs, RAG architectures, vector databases, MCP servers, and multiagent frameworks to deliver reliable and context aware GenAI solutions.
- Build intelligent document processing solutions using OCR technologies such as Azure Form Recognizer, Document Intelligence, Tesseract, and computer vision methods.
- Analyze complex technical issues end to end, identify root causes, and implement scalable, long term corrective solutions.
- Build and operate cloud native GenAI platforms within Azure environments while adhering to enterprise architectural, governance, and compliance standards.
- Design and deploy end-to-end data pipelines, machine learning models, and GenAI applications using Azure, Databricks, and Azure Machine Learning.
- Ensure consistent and reliable AI delivery by designing, building, and maintaining CI/CD pipelines for GenAI and ML workloads.




- Demonstrate hands-on expertise in containerization and orchestration using Docker and Podman.
- Apply software monitoring, scalability strategies, and quality management (QMS) practices to maintain high system reliability.
- Leverage DevOps toolchains including GitHub, GitHub Actions, Azure DevOps, and Kubernetes to enable productive development and deployment workflows.
- Implement security best practices and vulnerability management across AI models, applications, and infrastructure layers.
- Share domain and technical expertise through mentorship, peer collaboration, and cross training to strengthen team capabilities

Requirements
- 3-7+ Years of experience on Gen AI/Agentic AI, NLP, LLMs

Required Qualifications
- Bachelor's degree or higher in Computer Science, Engineering, or a related field, with a focus on Artificial Intelligence, Generative AI, NLP, or Language Processing
- LLMs, Azure OpenAI, Prompt engineering, Embeddings, Azure Document Intelligence, Retrieval Augmented Generation (RAG), LLM evaluation, Hallucination mitigation, Multiagent frameworks, agent orchestration, autonomous agents, workflow coordination, planner-executor patterns, complex AI workflow design
- Hands on experience with LangChain, LlamaIndex, Hugging Face Transformers, LangGraph, CrewAI, and AutoGen
- Root cause analysis, system design trade offs, optimization for cost, performance, and scalability.
- Experience building NLP and LLM powered solutions for text classification, entity extraction (NER), semantic search, summarization, and question answering in enterprise applications
- AI security best practices, vulnerability management, access control, compliance, responsible AI governance
- Familiarity with UI tools like Streamlit, Chainlit, and API Frameworks like Flask, Fast APIs, Rest APIs, React

📌 Data Scientist (Noida)
🏢 Optum
📍 Noida