Key Responsibilities :

AI Application Development :

- Design, develop, and deploy AI-powered applications using Foundation Models and Large Language Models (LLMs).

- Build intelligent agentic applications capable of autonomous task execution and workflow automation.

- Develop enterprise-grade Generative AI solutions to solve business challenges.

- Integrate AI capabilities into existing applications and digital platforms.

Agentic AI & Prompt Engineering :

- Develop AI agents using modern agent frameworks and orchestration platforms.

- Design effective prompts and prompt chains to improve model accuracy and response quality.

- Implement Retrieval-Augmented Generation (RAG) techniques and contextual AI workflows.

- Optimize agent performance, reasoning, and task execution capabilities.

Software Engineering & API Development :

- Build scalable backend services and RESTful APIs for AI applications.

- Develop reusable components and services to support AI-enabled products.

- Integrate foundation models with enterprise systems, databases, and external services.

- Ensure application performance, maintainability, and security.

Cloud Deployment & Operations :

- Deploy AI solutions on Microsoft Azure cloud platforms.

- Manage application deployment, monitoring, scaling, and maintenance.

- Utilize cloud-native services for model hosting, API management, and data processing.

- Implement best practices for cloud security, reliability, and cost optimization.

AI SDLC & Testing :

- Utilize AI Software Development Lifecycle (AI SDLC) tools and methodologies.

- Perform model testing, validation, performance tuning, and quality assurance.

- Conduct functional, integration, and performance testing for AI applications.

- Monitor deployed AI systems and implement continuous improvements.

Model Integration & Optimization :

- Integrate foundation models and AI services into production environments.

- Optimize model inference performance, latency, and scalability.

- Evaluate different AI models and select appropriate solutions for business requirements.

- Support model lifecycle management and version control.

Required Skills :

- Generative AI & AI Technologies

- Generative AI

- Foundation Models

- Large Language Models (LLMs)

- Agentic AI

- Prompt Engineering

- AI Application Development

- Model Integration & Optimization

- Software Engineering

- Python

- API Development

- RESTful Services

- Microservices Architecture

- Software Design Principles

- Cloud & DevOps

- Microsoft Azure

- Azure AI Services

- Azure OpenAI

- Cloud Deployment

- CI/CD Pipelines

- Containerization (Docker/Kubernetes preferred)

- Development Practices

- AI SDLC Tools

- Testing & Validation

- Performance Optimization

- Application Monitoring

Preferred Skills :

- LangChain

- LangGraph

- Semantic Kernel

- Vector Databases

- RAG (Retrieval-Augmented Generation)

- Azure Machine Learning

- Azure Cognitive Services

- GitHub Copilot

- MLOps Practices

Soft Skills :

- Strong analytical and problem-solving abilities

- Excellent communication and collaboration skills

- Ability to work independently and within cross-functional teams

- Strong ownership and delivery focus

- Client-facing and stakeholder management capabilities

Experience Requirements :

- 8+ years of overall software engineering experience.

- 4+ years of experience in AI/ML or Generative AI application development.

- Hands-on experience building and deploying AI-powered applications in Azure environments.

- Experience working with Foundation Models, LLMs, and Agentic AI frameworks.