AIML Engineer

Innodata India Private Limited; Dadri; On-site; INR 800,000 - 1,800,000; Full time

Job summary

Innodata India Private Limited seeks an experienced AI/ML Engineer located in Dadri, Uttar Pradesh. The ideal candidate will be instrumental in designing and implementing scalable AI systems, focusing on end-to-end machine learning solutions. Responsibilities include architecting distributed inference pipelines and optimizing AI systems for performance and cost. Applicants should be ready to work on agentic AI solutions and multi-agent workflows, ensuring high reliability and performance of applications.

Responsibilities

• Design end-to-end AI/ML and LLM system architecture.
• Define scalable architecture for AI applications.
• Select appropriate models, frameworks, and infrastructure.
• Implement advanced agent orchestration workflows.
• Build planning, reasoning, and memory-enabled agents.
• Design MCP server architecture and tool ecosystems.
• Define MCP governance and best practices.
• Architect distributed inference pipelines.
• Optimize latency, performance, and cost of AI systems.
• Design scalable vector database architecture.

Job description

The AI/ML Engineer will be responsible for designing and implementing scalable AI systems, including end-to end machine learning and LLM-based architectures. The role involves building agentic AI solutions, multi-agent workflows, and Model Context Protocol (MCP) integrations, while ensuring high performance, reliability, and scalability of AI applications.

Key Responsibilities

• Design end-to-end AI/ML and LLM system architecture.
• Define scalable architecture for AI applications.
• Select appropriate models, frameworks, and infrastructure. Agentic AI System Architecture
• Implement advanced agent orchestration workflows.
• Build planning, reasoning, and memory-enabled agents.
• Design MCP server architecture and tool ecosystems.
• Define MCP governance and best practices.

Scalable AI Infrastructure

• Architect distributed inference pipelines.
• Optimize latency, performance, and cost of AI systems.
• Design scalable vector database architecture.