AI Engineer/ Senior AI Engineer/ Lead AI Engineer

Verisk Analytics, Inc.; Hyderabad; Hybrid; INR 1,500,000 - 2,100,000; Full time

Job summary

We are seeking AI engineering professionals with strong software-development foundations to design, build, and deploy production-grade AI systems powered by large language models and intelligent agents. The role combines hands-on development with opportunities to contribute to architecture, engineering standards, and mentorship, with ownership aligned to experience.

The candidate will design scalable AI workflows, integrate AI capabilities with existing applications, and translate emerging

Qualifications

• 4-10 years of hands-on experience building applications using Generative AI or Agentic AI systems.
• Strong proficiency in Python and backend engineering principles.
• Experience designing RESTful APIs and scalable service architectures.
• Familiarity with agent frameworks and autonomous decision-making workflows.

Responsibilities

• Design, develop, deploy, and monitor production-ready applications leveraging Generative AI and Agentic AI frameworks.
• Build intelligent agents capable of reasoning, planning, and multi-step task execution using LLMs, tool calling, multi-agent coordination, and orchestration pipelines.
• Integrate AI applications and workflows with existing enterprise systems.
• Implement prompt-engineering strategies, retrieval-augmented generation (RAG), embeddings, vector search, and contextual memory systems.
• Apply Model Context Protocol (MCP) patterns to enable structured communication between AI agents, tools, and systems.
• Utilize AI-assisted engineering approaches, including context-driven and specification-driven development.
• Develop evaluation pipelines to measure the accuracy, safety, reliability, and performance of AI systems.
• Optimize AI-powered workflows for latency, cost efficiency, and scalability.
• Ensure production reliability through testing, logging, monitoring, and observability of AI behaviour.
• Contribute to architectural decisions, coding standards, reusable patterns, and engineering best practices.
• Collaborate with product managers, data teams, and software engineers to deliver AI-powered features.
• Provide technical guidance, share knowledge, and support developer mentorship as appropriate to experience.
• Apply AI-governance principles covering responsible model usage, data handling, auditability, transparency, and risk mitigation.
• Promote AI-driven engineering practices across development teams.
• Research emerging AI tools and frameworks, document workflows, and maintain reproducible engineering processes.

Skills

.NET

React

Full-stack development

Python

Backend engineering

RESTful APIs

LLM APIs

Vector search

RAG pipelines

Agent frameworks

Data structures

Performance optimization

Analytical thinking

Collaboration

Tools

Docker

Kubernetes

Git

CI/CD pipelines

AWS

Job description

Job Overview

We are seeking AI engineering professionals with strong software-development foundations and hands-on experience in Generative AI and Agentic AI to design, build, and deploy production-grade AI systems powered by large language models and intelligent agents.

You will develop scalable AI workflows, integrate AI capabilities with existing applications, and translate emerging technologies into reliable business solutions. The role combines hands-on development with opportunities to contribute to architecture, engineering standards, and technical mentorship, with ownership aligned to experience.

Key Responsibilities

• Design, develop, deploy, and monitor production-ready applications leveraging Generative AI and Agentic AI frameworks.
• Build intelligent agents capable of reasoning, planning, and multi-step task execution using LLMs, tool calling, multi-agent coordination, and orchestration pipelines.
• Integrate AI applications and workflows with existing enterprise systems.
• Implement prompt-engineering strategies, retrieval-augmented generation (RAG), embeddings, vector search, and contextual memory systems.
• Apply Model Context Protocol (MCP) patterns to enable structured communication between AI agents, tools, and systems.
• Utilize AI-assisted engineering approaches, including context-driven and specification-driven development.
• Develop evaluation pipelines to measure the accuracy, safety, reliability, and performance of AI systems.
• Optimize AI-powered workflows for latency, cost efficiency, and scalability.
• Ensure production reliability through testing, logging, monitoring, and observability of AI behaviour.
• Contribute to architectural decisions, coding standards, reusable patterns, and engineering best practices.
• Collaborate with product managers, data teams, and software engineers to deliver AI-powered features.
• Provide technical guidance, share knowledge, and support developer mentorship as appropriate to experience.
• Apply AI-governance principles covering responsible model usage, data handling, auditability, transparency, and risk mitigation.
• Promote AI-driven engineering practices across development teams.
• Research emerging AI tools and frameworks, document workflows, and maintain reproducible engineering processes.

Required Qualifications

• Professional software-development experience in .NET, React, or full-stack development.
• 4-10 years of hands-on experience building applications using Generative AI or Agentic AI systems.
• Strong proficiency in Python and backend engineering principles.
• Experience designing RESTful APIs and scalable service architectures.
• Practical experience with LLM APIs, embeddings, vector search, or RAG pipelines.
• Familiarity with agent frameworks and autonomous decision-making workflows.
• Strong understanding of data structures, system design, and performance optimization.
• Strong analytical thinking, problem-solving, and collaboration skills.

Preferred Qualifications

• Experience deploying AI applications in cloud environments, preferably AWS.
• Familiarity with vector databases and knowledge-retrieval architectures.
• Experience with Git, CI/CD pipelines, and production deployments.
• Exposure to Docker and Kubernetes.
• Experience implementing evaluation or monitoring pipelines for AI agents.
• Knowledge of AI safety, guardrails, responsible AI, and governance controls.
• Knowledge of distributed systems or event-driven architecture.
• Experience owning architectural decisions, mentoring developers, or providing technical leadership.