Description

Key Responsibilities

Design, develop, and implement enterprise-grade Generative AI applications using LLMs, RAG architectures, prompt engineering, and Agentic AI concepts.

Build intelligent AI pipelines and workflows using frameworks such as LangChain, LangGraph, and Microsoft Foundry Agent Service.

Evaluate and optimize GenAI solutions for performance, accuracy, scalability, and reliability.

Contribute to the end-to-end GenAI lifecycle, including solution design, development, integration, deployment, performance tuning, and optimization.

Support secure deployment, horizontal scaling, and operational stability of GenAI workloads.

Implement monitoring, logging, and observability practices for production AI environments.

Develop and deploy GenAI systems across cloud platforms, particularly AWS and Azure.

Contribute to distributed system design for scalable AI workloads.

Implement containerization, orchestration, and Infrastructure as Code practices.

Ensure enterprise solutions meet availability, performance, security, and reliability requirements.

Develop scalable applications using Python and microservices-based architecture.

Apply secure coding standards and appropriate data-handling practices for enterprise and regulated environments.

Contribute to CI/CD pipelines, automated testing, and deployment workflows.

Participate in code reviews and follow software engineering best practices.

Required Qualifications

5+ years of experience developing and implementing GenAI applications leveraging:

Large Language Models (LLMs)

Retrieval-Augmented Generation (RAG)

Prompt Engineering

Agentic AI concepts and workflows

5+ years of experience building intelligent pipelines using:

LangChain

LangGraph

Microsoft Foundry Agent Service

5+ years of experience evaluating GenAI solution performance, accuracy, and scalability.

5+ years of experience across the end-to-end GenAI lifecycle, including solution design, development, integration, deployment, and optimization.

5+ years of experience supporting secure deployment, horizontal scaling, and operational stability of GenAI workloads.

5+ years of experience with monitoring, logging, and observability practices.

5+ years of experience developing and deploying GenAI systems across cloud platforms, including AWS and Azure.

5+ years of experience with distributed system design for scalable AI workloads.

5+ years of experience with modern infrastructure practices:

Docker

Kubernetes

Terraform

ARM/Bicep

5+ years of experience developing scalable applications using Python and microservices architecture.

5+ years of experience applying secure coding standards and data-handling practices in enterprise or regulated environments.

5+ years of experience with CI/CD pipelines, automated testing, and deployment workflows.

Experience participating in code reviews and following engineering best practices.

Education

Bachelor’s degree in Computer Science or equivalent work experience

Key Skills

Education

Bachelor's degree