Generative AI Developer

Location : Remote

Experience : 4-6 years

Position : Gen-AI Developer (Hands-on)

Technical Requirements :

- Hands-on Data Science , Agentic AI, AI/Gen AI / ML /NLP

- Azure services (App Services, Containers, AI Foundry, AI Search, Bot Services)

- Experience in C#

- Semantic Kernel

- Strong background in working with LLMs and building Gen AI applications

- AI agent concepts

- .NET Aspire

- End-to-end environment setup for ML/LLM/Agentic AI (Dev/Prod/Test)

- Machine Learning & LLM deployment and development

- Model training, fine-tuning, and deployment

- Kubernetes, Docker, Serverless architecture

- Infrastructure as Code (Terraform, Azure Resource Manager)

- Performance Optimization & Cost Management

- Cloud cost management & resource optimization, auto-scaling

- Cost efficiency strategies for cloud resources

- MLOps frameworks (Kubeflow, MLflow, TFX)

- Large language model fine-tuning and optimization

- Data pipelines (Apache Airflow, Kafka, Azure Data Factory)

- Data storage (SQL/NoSQL, Data Lakes, Data Warehouses)

- Data processing and ETL workflows

- Cloud security practices (VPCs, firewalls, IAM)

- Secure cloud architecture and data privacy

- CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins)

- Automated testing and deployment for ML models

- Agile methodologies (Scrum, Kanban)

- Cross-functional team collaboration and sprint management

- Experience with model fine-tuning and infrastructure setup for local LLMs

- Custom model training and deployment pipeline design

- Good communication skills (written and oral)

Key Result Areas (KRAs) :

- Timely delivery of Gen AI and LLM-based solutions from design to deployment.

- Uptime and reliability of deployed AI applications

- Achieve targeted performance metrics (accuracy, latency, throughput) for deployed models.

- Regularly improve and fine-tune models using feedback loops

- Maintain efficient use of cloud resources with cost reduction initiatives.

- Implement auto-scaling and resource optimization strategies.

- Contribute to the development of POCs (Proof of Concepts) for emerging AI solutions.

- Experiment with new frameworks, APIs, and methodologies (e.g., Semantic Kernel, AI Foundry).

- Ensure smooth, automated deployment pipelines for ML models using Azure DevOps/GitHub.

- Minimize downtime during releases and model updates