IN_Director_AI Architect_Data and Analytics_Advisory_Bangalore

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:

. 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

Years of experience required: 8-16 years

Education qualification: B.Tech/MCA/BCA/M.tech