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,pytorch/tensor flow/keras, Langchain, Langgraph, Docker, Kunernetes 

Preferred skill sets: 

• SQL,machine learning, data science 

Years of experience required:

3+ 

Education qualification: 

BE/B.Tech/MBA/MCA