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.
Skills 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 Skilled 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
Years of experience required: 5-8 years
Education qualification: B.Tech/MCA/BCA/M.tech

📌 Artificial Intelligence Engineer (Uttar Pradesh)
🏢 PwC India
📍 Uttar Pradesh