GenAI Technical Architect

Coforge; Dadri; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Coforge is seeking a GenAI Architect to design, implement, and scale enterprise AI/ML solutions. You will oversee data prep, model training, deployment, and production support, working with business and engineering teams.

The role requires leadership in GenAI, experience with LLMs, and strong Python/MLOps skills in a fast-paced environment.

Qualifications

• Minimum 10+ years in AI/ML with enterprise-scale design
• Hands-on experience with GenAI, LLMs, and prompt engineering
• Strong Python and MLops practices

Responsibilities

• Design, develop, and deploy enterprise-scale Generative AI solutions.
• Lead AI systems using LLMs, agentic AI frameworks, and cloud-native services.
• Own end-to-end AI/ML project lifecycle from data prep to deployment.

Skills

GenAI architectures

Python

Cloud fundamentals

Tools

Azure OpenAI

AWS Bedrock

Google Vertex AI

Job description

Experience - 10 - 18 years

Locations - Greater Noida

Work Mode - 5 Days WFO

We at Coforge are hiring for GenAI Architect with the following skillset:

• Design, develop, and deploy enterprise-scale Generative AI and AI/ML solutions.
• Lead the development of AI systems leveraging Large Language Models (LLMs), Agentic AI frameworks, and cloud-native AI services.
• Drive end-to-end ownership of AI/ML solutions from data preparation and model training to deployment and production support.
• Build, customize, fine-tune, and optimize Large Language Models using Azure OpenAI, AWS Bedrock, Google Vertex AI, and other GenAI platforms.
• Design and implement Retrieval Augmented Generation (RAG) frameworks, vector databases, embeddings, and prompt engineering strategies.
• Perform data collection, data mining, preprocessing, and analysis of structured and unstructured datasets.
• Develop scalable AI architectures leveraging cloud services, MLOps practices, and distributed computing frameworks.
• Collaborate with business stakeholders, product teams, and engineering teams to translate business problems into AI-powered solutions.
• Establish best practices around AI model governance, security, performance optimization, evaluation, and observability.
• Mentor development teams on AI/ML technologies, modern Python practices, and Generative AI implementation patterns.