Gen AI Engineer

Quess Corp Limited; Hyderabad; On-site; INR 3,500,000 - 5,200,000; Full time

Job summary

Quess Corp Limited is seeking an experienced Architect to design end-to-end GenAI/LLM solutions across AWS services such as Bedrock, SageMaker, Lambda, ECS/EKS and S3. You will translate business needs into scalable AI architectures.

You will lead deployment of production LLMs, optimize performance and cost, mentor engineering teams, and establish MLOps practices, governance and secure, compliant workflows in enterprise environments.

Responsibilities

• Architect and design end-to-end GenAI/LLM solutions leveraging AWS services such as Bedrock, SageMaker, Lambda, ECS/EKS, and S3
• Lead deployment of LLMs into production environments, ensuring scalability, low latency, cost-efficiency, and reliability
• Design and build Agentic AI systems — multi-agent orchestration, tool-calling, autonomous task planning, and agent-to-agent communication
• Design and implement RAG pipelines, vector databases, and prompt engineering strategies for enterprise use cases
• Fine-tune, evaluate, and optimize LLMs for performance, cost, and accuracy
• Develop robust, production-grade Python code for GenAI pipelines, APIs, and agent frameworks
• Define best practices for LLMOps/MLOps, including CI/CD pipelines, model monitoring, versioning, and governance
• Ensure solutions are secure, scalable, and compliant with enterprise architecture and data privacy standards
• Collaborate with cross-functional teams to translate business requirements into technical architecture
• Evaluate emerging GenAI/Agentic AI tools, frameworks, and AWS services, and provide recommendations for adoption
• Mentor engineering teams on GenAI, Agentic AI, and AWS architecture best practices
• Create technical documentation, architecture diagrams, and present solutions to technical and non-technical stakeholders

Job description

• Architect and design end-to-end GenAI/LLM solutions leveraging AWS services such as Amazon Bedrock, SageMaker, Lambda, ECS/EKS, and S3
• Lead deployment of LLMs into production environments, ensuring scalability, low latency, cost-efficiency, and reliability
• Design and build Agentic AI systems — multi-agent orchestration, tool-calling, autonomous task planning, and agent-to-agent communication
• Design and implement RAG (Retrieval-Augmented Generation) pipelines, vector databases, and prompt engineering strategies for enterprise use cases
• Fine-tune, evaluate, and optimize LLMs for performance, cost, and accuracy
• Develop robust, production-grade Python code for GenAI pipelines, APIs, and agent frameworks
• Define best practices for LLMOps/MLOps, including CI/CD pipelines, model monitoring, versioning, and governance
• Ensure solutions are secure, scalable, and compliant with enterprise architecture and data privacy standards
• Collaborate with cross-functional teams (data engineers, ML engineers, product managers) to translate business requirements into technical architecture
• Evaluate emerging GenAI/Agentic AI tools, frameworks, and AWS services, and provide recommendations for adoption
• Mentor engineering teams on GenAI, Agentic AI, and AWS architecture best practices.
• Create technical documentation, architecture diagrams, and present solutions to technical and non-technical stakeholders