Project Description: Grid Dynamics aims building enterprise generative AI framework to deliver innovative, scalable and efficient AI-driven solutions across business functions.Due to constant scaling of digital capabilities the platform requires enhancements to incorporate cutting-edge generative AI features and meet emerging business demands. Platform should onboard brand new capabilities like Similarity Search (image,video and voice);Ontology and entity managment;voice and file mgmt [text to speech & vice-versa, metadata tagging, multi-media file support];Advanced RAG ; Multi-Modal capabilities Responsibilities: As an LLMOps Engineer, you will be responsible for providing expertise on overseeing the complete lifecycle management of large language models (LLM). This includes the development of strategies for deployment, continuous integration and delivery (CI/CD) processes, performance tuning, and ensuring high availability of our LLM services. You will collaborate closely with data scientists, AI/ML engineers, and IT teams to define and align LLM operations with business goals, ensuring a seamless and efficient operating model. In this role, you will: Define and disseminate LLMOps best practices. Evaluate and compare different LLMOps tools to incorporate the best practices. Stay updated on industry trends and advancements in LLM technologies and operational methodologies. Participate in architecture design/validation sessions for the Generative AI use cases with entities. Contribute to the development and expansion of GenAI use cases, including standard processes, framework, templates, libraries, and best practices around GenAI. Design, implement, and oversee the infrastructure required for the efficient operation of large language models in collaboration with client entities. Provide expertise and guidance to client entities in the development and scaling of GenAI use cases, including standard processes, framework, templates, libraries, and best practices around GenAI Serve as the expert and representative on LLMops Practices, including: (1) Developing and maintaining CI/CD pipelines for LLM deployment and updates. (2) Monitoring LLM performance, identifying and resolving bottlenecks, and implementing optimizations. (3) Ensuring the security of LLM operations through comprehensive risk assessments and the implementation of robust security measures. Collaborate with data and IT teams to facilitate data collection, preparation, and model training processes. Practical experience with training, tuning, utilizing LLMs/SLMs. Strong experience with GenAI/LLM frameworks and techniques, like guardrails, Langchain, etc. Knowledge of LLM security and observability principles. Experience of using Azure cloud services for ML Experience of using Azure cloud services for ML Min requirements: Programming languages: Python Public Cloud: Azure Frameworks: K8s, Terraform, Arize or any other ML/LLM observability tool Experience: Experience with public services like Open AI, Anthropic and similar, experience deploying open source LLMs will be a plus Tools: LangSmith/LangChain,guardrails Would be a plus: Knowledge of LLMOps best practices. Experience with monitoring/logging for production models (e.g. Prometheus, Grafana, ELK stack) We offer: Opportunity to work on bleeding-edge projects Work with a highly motivated and dedicated team Competitive salary Flexible schedule Benefits package - medical insurance, sports Corporate social events Professional development opportunities Well-equipped office
