& Summary: A Career within............

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:

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

Preferred skill sets:

• SQL,machine learning,data science

Years of experience required:

3+

Education qualification:

BE/B.Tech/MBAMCA