Applied AI Engineer

Clarus Advisers; Hyderabad; On-site; INR 1,400,000 - 2,100,000; Full time

Job summary

Clarus Advisers seeks an Applied AI Engineer - Software Specialist Engineer to design, develop, and deploy production-grade AI/GenAI applications. You will work across the full software lifecycle, integrating LLMs, RAG pipelines, vector databases, AI agents, and cloud AI services.

The role combines strong software fundamentals with practical experience in Generative AI, agentic applications, cloud-native architecture, DevSecOps, and AI-augmented development practices.

Qualifications

• 6-9 years of overall software engineering experience.
• 3+ years of hands-on AI/ML, Generative AI, and/or agentic apps.
• Strong programming experience in Python and SQL.
• Experience with LLMs, RAG, prompt engineering, vector databases and LLM evaluation.
• Experience integrating OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, or open-source LLMs.
• Experience with LangChain, LangGraph, or equivalent orchestration frameworks.
• 3+ years of cloud-native engineering on Azure/AWS/GCP.
• Experience with microservices, PaaS/FaaS, APIs, IaC, and CI/CD.
• Knowledge of PyTorch/TensorFlow is desirable.
• Experience with ML observability tools like MLflow, LangFuse, LangSmith.
• Strong understanding of OOP/OOD, data structures, algorithms, system design, and testing.
• Experience with DevSecOps, SRE, SonarQube.
• Bachelor's degree in related field.

Responsibilities

• Design and develop production-ready Generative AI and agentic AI applications.
• Build LLM-powered solutions using OpenAI, Anthropic, open-source models, Azure OpenAI, AWS Bedrock, or equivalent platforms.
• Develop RAG pipelines, prompt engineering workflows, vector search, and knowledge-based AI applications.
• Build and orchestrate AI agents using frameworks such as LangChain, LangGraph, LangFuse, LangSmith, or equivalent tools.
• Develop scalable cloud-native applications using Azure, AWS, or GCP.
• Implement microservices, PaaS/FaaS architectures, APIs, and application-level infrastructure as code.
• Apply AI/ML evaluation, monitoring, tracing, and observability practices.
• Develop high-quality, maintainable software using strong OOP/OOD, DSA, testing, and code instrumentation practices.
• Collaborate on architecture and technical designs using business context, sequence, activity, state, entity relationship, and data-flow diagrams.
• Implement CI/CD, DevSecOps, SRE, security, and quality engineering practices.
• Use tools such as GitHub, Azure DevOps, SonarQube, MLflow, and related platforms.
• Apply cost-aware engineering and FinOps principles to cloud and AI workloads.
• Contribute to AI-augmented and spec-driven software development.
• Work closely with cross-functional teams to translate business requirements into scalable AI solutions.

Skills

Python

SQL

Generative AI/LLMs

Agentic AI

Cloud-native eng.

OOP/OOD

DSA

DevSecOps

CI/CD

PyTorch/TensorFlow

Education

Bachelor's degree in Computer Science / Software Engineering / Data Science

Tools

LangChain

LangGraph

LangFuse

LangSmith

GitHub

Azure DevOps

SonarQube

MLflow

Vector databases

Job description

Company Overview

Our client is a leading technology-driven organization focused on building innovative, cloud-native software products and intelligent AI-powered solutions.

Company Overview

Our client is a leading technology-driven organization focused on building innovative, cloud-native software products and intelligent AI-powered solutions.

Position Overview

As an Applied AI Engineer - Software Specialist Engineer, you will design, develop, and deploy production-grade AI/GenAI applications. You will work across the full software development lifecycle, integrating LLMs, RAG pipelines, vector databases, AI agents, and cloud AI services. The role requires strong software engineering fundamentals combined with practical experience in Generative AI, agentic applications, cloud-native architecture, DevSecOps, and AI-augmented development practices.

Responsibilities

• Design and develop production-ready Generative AI and agentic AI applications.
• Build LLM-powered solutions using OpenAI, Anthropic, open-source models, Azure OpenAI, AWS Bedrock, or equivalent platforms.
• Develop RAG pipelines, prompt engineering workflows, vector search, and knowledge-based AI applications.
• Build and orchestrate AI agents using frameworks such as LangChain, LangGraph, LangFuse, LangSmith, or equivalent tools.
• Develop scalable cloud-native applications using Azure, AWS, or GCP.
• Implement microservices, PaaS/FaaS architectures, APIs, and application-level infrastructure as code.
• Apply AI/ML evaluation, monitoring, tracing, and observability practices.
• Develop high-quality, maintainable software using strong OOP/OOD, DSA, testing, and code instrumentation practices.
• Collaborate on architecture and technical designs using business context, sequence, activity, state, entity relationship, and data-flow diagrams.
• Implement CI/CD, DevSecOps, SRE, security, and quality engineering practices.
• Use tools such as GitHub, Azure DevOps, SonarQube, MLflow, and related engineering platforms.
• Apply cost-aware engineering and FinOps principles to cloud and AI workloads.
• Contribute to AI-augmented and spec-driven software development.
• Work closely with cross-functional teams to translate business requirements into scalable AI solutions.

Skills & Experience

• 6-9 years of overall software engineering experience.
• 3+ years of hands-on experience building AI/ML, Generative AI, and/or agentic applications.
• Strong programming experience in Python and SQL.
• Experience with one or more of C#/ Java, NodeJS, Angular, or React.
• Strong hands-on experience with LLMs, RAG, prompt engineering, vector databases, and LLM evaluation.
• Experience integrating OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, or open-source LLMs.
• Experience with LangChain, LangGraph, or equivalent agent orchestration frameworks.
• 3+ years of experience with Azure, AWS, or GCP and cloud-native engineering.
• Experience with microservices, PaaS/FaaS, APIs, Infrastructure as Code, and CI/CD.
• Knowledge of PyTorch/TensorFlow is desirable.
• Experience with MLflow, LangFuse, LangSmith, or equivalent AI observability/evaluation tools.
• Strong understanding of OOP/OOD, data structures, algorithms, system design, and software testing.
• Experience with DevSecOps, SRE, XP/Lean, GitHub/Azure DevOps, and SonarQube.
• Understanding of cloud/AI cost optimization and FinOps.
• Bachelor's degree in Computer Science, Software Engineering, Data Science, Machine Learning, or a related discipline.