Generative AI & MLOps Lead

0 years

0 Lacs

Posted:1 day ago| Platform: Linkedin logo

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Work Mode

Remote

Job Type

Full Time

Job Description

We are currently hiring for a Generative AI & MLOps Lead role, and your profile looks like a strong fit for this opportunity. Please find the job details below and share your updated resume along with the requested details.

Position :

Generative AI & MLOps LeadWe are looking for an experienced Generative AI & MLOps Lead to define and drive the architecture, technical strategy, and deployment of production-ready Generative AI systems. The ideal candidate will have a solid background in Python, Azure OpenAI, LangChain, and MLOps with proven experience in leading AI teams and projects.

Location :

Work from Home (Remote India)

Working Hours :

Europe Time Zone

Need :

Immediate Joiners

Key Responsibilities

  • Technical Leadership : Define the overall technical strategy and architecture for Generative AI solutions aligned with business goals.
  • Azure OpenAI Strategy : Design and implement solutions using Azure OpenAI (GPT models, embeddings) including model selection, fine-tuning, and prompt engineering.
  • Architecture & Governance : Oversee the development of scalable and secure AI chatbot builds and RAG (Retrieval-Augmented Generation) systems.
  • MLOps & DevOps Oversight : Drive implementation of CI/CD, automated testing, and deployment pipelines to ensure reliable AI model delivery on Azure.
  • Team Management : Mentor developers, review Python code, maintain standards, and manage backlog for AI initiatives.

Required Skills

  • Expert-level proficiency in Python and AI/ML frameworks (LangChain, Semantic Kernel, Hugging Face, PyTorch, TensorFlow).
  • Deep expertise in Azure OpenAI Service and the Azure AI ecosystem.
  • Proven experience in custom chatbot / conversational AI system development.
  • Strong background in DevOps / MLOps tools (Azure DevOps, GitHub Actions, Terraform, MLflow, Docker, Kubernetes).
  • Hands-on experience in building and deploying RAG-based systems and production AI pipelines.
(ref:hirist.tech)

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