Responsibilities:  ML Pipeline Design: · Design ML pipelines for experiment management, model management, feature management, and model retraining. · Design APIs for model inferencing at scale. · Proven expertise with MLflow, SageMaker, Vertex AI, and Azure AI. · LLM Serving and GPU Architecture: · Possess deep knowledge of GPU architectures. · Expertise in distributed training and serving of large language models. · Proficient in model and data parallel training using frameworks like DeepSpeed and service frameworks like vLLM. · Model Fine-Tuning and Optimization: · Demonstrate proven expertise in model fine-tuning and optimization techniques. · Achieve better latencies and accuracies in model results. · Reduce training and resource requirements for fine-tuning LLM and LVM models. · DevOps and LLMOps Proficiency: · Proven expertise in DevOps and LLMOps practices. · Knowledgeable in Kubernetes, Docker, and container orchestration. · Deep understanding of LLM orchestration frameworks like Flowise, Langflow, and Langgraph. · Skill Matrix · LLM: Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama · LLM Ops: ML Flow, Langchain, Langraph, LangFlow, Flowise, LLamaIndex, SageMaker, AWS Bedrock, Vertex AI, Azure AI · Databases/Datawarehouse: DynamoDB, Cosmos, MongoDB, RDS, MySQL, PostGreSQL, Aurora, Spanner, Google BigQuery. · Cloud Knowledge: AWS/Azure/GCP · Dev Ops (Knowledge): Kubernetes, Docker, FluentD, Kibana, Grafana, Prometheus · Cloud Certifications (Bonus): AWS Professional Solution Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert · Proficient in Python, SQL, Javascript

Mandatory skill sets:  • ML Pipeline Design: · • Design ML pipelines for experiment management, model management, feature management, and model retraining. • · Design APIs for model inferencing at scale. · Proven expertise with MLflow, SageMaker, Vertex AI, and Azure AI. • · LLM Serving and GPU Architecture: · Possess deep knowledge of GPU architectures. • · Expertise in distributed training and serving of large language models. • · Proficient in model and data parallel training using frameworks like DeepSpeed and service frameworks like vLLM. • · Model Fine-Tuning and Optimization: · Demonstrate proven expertise in model fine-tuning and optimization techniques. • · Achieve better latencies and accuracies in model results. · Reduce training and resource requirements for fine-tuning LLM and LVM models. · DevOps and LLMOps Proficiency: · Proven expertise in DevOps and LLMOps practices. • · Knowledgeable in Kubernetes, Docker, and container orchestration. • · Deep understanding of LLM orchestration frameworks like Flowise, Langflow, and Langgraph. • · Skill Matrix · LLM: Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama • · LLM Ops: ML Flow, Langchain, Langraph, LangFlow, Flowise, LLamaIndex, SageMaker, AWS Bedrock, Vertex AI, Azure AI · • Databases/Datawarehouse: DynamoDB, Cosmos, MongoDB, RDS, MySQL, PostGreSQL, Aurora, Spanner, Google BigQuery. · Cloud Knowledge: AWS/Azure/GCP · Dev Ops (Knowledge): Kubernetes, Docker, FluentD, Kibana, Grafana, Prometheus · Cloud Certifications (Bonus): AWS Professional Solution Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert · Proficient in Python, SQL, Javascript

Preferred skill sets: 

• Gen AI

Years of experience required:

8-16 years

Education qualification: 

B.Tech/MCA/BCA/M.tech