Role & responsibilities

AI & Generative AI Architecture

- Design and implement enterprise-grade AI solutions leveraging Large Language Models (LLMs), Agentic AI frameworks, and generative AI capabilities.
- Architect multi-agent AI systems using frameworks such as LangGraph, LangChain, and Hugging Face.
- Build Retrieval Augmented Generation (RAG) architectures integrating LLMs with enterprise knowledge repositories.
- Implement prompt engineering strategies, embeddings, and vector search for conversational AI and intelligent assistants.

Cloud & Data Engineering (AWS)

- Design and deploy AI and data platforms on AWS cloud infrastructure.
- Leverage services such as:
- Amazon Bedrock / SageMaker for AI/ML model development and deployment
- Amazon OpenSearch / Vector DBs for semantic search
- AWS Lambda, Step Functions, and API Gateway for orchestration
- Amazon S3 and AWS Glue for data engineering pipelines

- Develop scalable ETL/ELT pipelines using PySpark and distributed processing frameworks.
- Implement contemporary data architectures including Lakehouse and Medallion architectures.

Intelligent Automation

- Architect and implement enterprise automation solutions using RPA platforms such as: UiPath / Blue Prism / Robocorp / SAP iRPA / Power Automate / Intelligent Document Processing (IDP)




- Integrate AI/ML capabilities with RPA to build hyperautomation solutions.
- Automate business processes across Web, Windows, Mainframe, SAP, and API systems.

AI Platform Engineering

- Build AI-driven applications using Python-based frameworks such as FastAPI, Flask, or Django.
- Develop microservices-based AI architectures integrated with enterprise systems.
- Implement MLOps practices including experiment tracking, model lifecycle management, and deployment automation.

Architecture & Delivery Leadership

- Lead solution architecture design and technical governance for AI and automation programs.
- Collaborate with business stakeholders, product teams, and engineering teams to translate requirements into scalable solutions.
- Define architecture standards, frameworks, and reusable assets for enterprise AI platforms.
- Mentor engineering teams and provide guidance on best practices for AI solution development and deployment.

Required Skills & Experience

- 1215+ years of experience in software engineering, automation, and solution architecture.
- Hands-on experience in Generative AI or Agentic AI implementations.
- Proven experience delivering enterprise-scale AI and automation solutions.

📌 Ai Architect (Gen Ai, Agentic Ai) (Delhi)
🏢 Espire Infolabs
📍 Delhi