& Summary:AIArchitect

Responsibilities:

• ML Pipeline Design:

• Design ML pipelines for experiment management, model management, feature management, and model retraining.

• Design APIs for model inferencing at scale.

• ProvenexpertisewithMLflow, SageMaker, Vertex AI, and Azure AI.

• LLM Serving and GPU Architecture:

• Possess deep knowledge of GPUarchitectures.

• Expertisein distributed training andserving oflarge language models.

• Proficient in model and data parallel training using frameworks likeDeepSpeedand service frameworks likevLLM.

• Model Fine-Tuning and Optimization:

• Demonstrate provenexpertisein model fine-tuning and optimization techniques.

• Achieve better latencies andaccuraciesin model results.

• Reduce training and resource requirements for fine-tuning LLM and LVM models.

• DevOps andLLMOpsProficiency:

• Provenexpertisein DevOps andLLMOpspractices.

• Knowledgeable in Kubernetes, Docker, and container orchestration.

• Deep understanding of LLM orchestration frameworks likeFlowise,Langflow, andLanggraph.

• 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, GoogleBigQuery.

• 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:

• GenAI,LLM,Huggingface,python,pytorchtensorflow/keras,Langchain,Langgraph,Docker,Kunernetes

Preferred skill sets:

• SQL,machinelearning,data science

Years of experiencerequired

3+

Education qualification:

BE/B.TechMBAMCA