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Generative AI Developer

Generative AI Developer

Aidewiser SoltekDelhi, IN
30+ days ago
Job type
  • Remote
Job description

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

    • (ref : hirist.tech)

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