Generative AI Developer - Data Modeling

Description :

Job Title : Gen - AI Developer

Experience : 4- 6 Years

Role Overview :

We are looking for an experienced Gen-AI Developer with a strong background in C#, Semantic Kernel, and Azure AI services to design, build, and deploy next-generation AI applications leveraging Large Language Models (LLMs), Agentic AI, and MLOps practices.

The ideal candidate should bring a mix of hands-on development, infrastructure management, and AI orchestration expertise to create scalable, secure, and cost-optimized Gen AI solutions.

Key Responsibilities :

AI Application Development :

- Design, develop, and optimize LLM-powered applications using C#, Semantic Kernel, and .NET Aspire.

- Implement AI agent architectures for conversational AI, decision-making, and automation workflows.

- Build custom pipelines for model training, fine-tuning, and deployment (LLMs, NLP, ML).

Cloud & Infrastructure :

- Deploy AI solutions using Azure Services (App Services, AI Foundry, Bot Services, AI Search, Containers).

- Set up end-to-end environments (Dev/Test/Prod) for ML/LLM/Agentic AI workloads.

- Implement serverless, containerized, and Kubernetes-based deployments for scalable AI applications.

- Use Infrastructure as Code (Terraform, ARM templates) for cloud provisioning and automation.

Data Engineering & Pipelines :

- Build ETL/data processing workflows using Apache Airflow, Kafka, Azure Data Factory.

- Integrate SQL/NoSQL, Data Lakes, and Data Warehouses into AI-driven applications.

- Manage data pipelines ensuring real-time ingestion, storage, and transformation.

MLOps & CI/CD :

- Establish MLOps frameworks (Kubeflow, MLflow, TFX) for training, deployment, and monitoring of ML/LLM models.

- Implement CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins) for AI models and services.

- Automate testing, monitoring, and retraining cycles to ensure continuous model improvement.

Optimization & Cost Management :

- Monitor and optimize cloud resource utilization to ensure cost efficiency and scalability.

- Implement auto-scaling strategies for AI workloads.

- Fine-tune large-scale models for performance and efficiency (latency, throughput, GPU/TPU usage).

Security & Compliance :

- Apply cloud security best practices (IAM, firewalls, VPCs, encryption).

- Ensure data privacy, secure access, and regulatory compliance in AI applications.

Collaboration & Delivery :

- Work in Agile environments (Scrum/Kanban) with cross-functional teams.

- Translate business needs into scalable Gen-AI solutions.

- Communicate complex AI concepts to both technical and non-technical stakeholders.

Technical Skills & Requirements :

Programming & AI Development :

- Strong C# and .NET experience (Mandatory).

- Hands-on with Semantic Kernel for AI orchestration.

- Expertise in LLM & Gen AI development.

- Familiarity with Agentic AI frameworks and autonomous agents.

Cloud & Infrastructure :

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

- Experience with Kubernetes, Docker, serverless architectures.

- IaC tools : Terraform, ARM templates.

AI/ML & Data Engineering :

- Strong experience in ML, NLP, and LLM development.

- Familiar with MLOps pipelines (Kubeflow, MLflow, TFX).

- Data pipeline tools : Apache Airflow, Kafka, Azure Data Factory.

- Databases : SQL, NoSQL, Data Lakes, Warehouses.

Deployment & Optimization :

- Experience with custom model training, fine-tuning, and inference optimization.

- Knowledge of GPU/TPU environments, distributed model training.

- Performance tuning & cost management for cloud-based AI workloads.

Security & Compliance :

- Deep understanding of cloud security frameworks, IAM, encryption.

- Best practices in secure AI architecture and data governance.

Other :

- Strong problem-solving and debugging skills.

- Excellent written and verbal communication skills.

- Ability to work in fast-paced, cross-functional teams.

Preferred Qualifications :

- Experience with local LLM deployments and custom fine-tuning.

- Exposure to open-source AI orchestration frameworks.

- Certifications in Azure AI, Cloud Security, or MLOps.

- Hands-on with AI product lifecycle management from POC ? Production