AI Engineer

• Build scalable AI platforms that are customer-facing.
• Evangelize the platform with customers and internal stakeholders.
• Ensure platform scalability, reliability, and performance to meet business needs.
• Design ML pipelines for experiment management, model management, feature management, and model retraining.
• Implement A/B testing of models.
• Design APIs for model inferencing at scale.
• Proven expertise with MLflow, Sage Maker, Vertex AI, and Azure AI.

• Serve as an SME in LLM serving paradigms.
• 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 Deep Speed and service frameworks like vLLM.

• 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.

• Have extensive knowledge of different LLM models.
• Provide insights on the applicability of each model based on use cases.
• Proven experience in delivering end-to-end solutions from engineering to production for specific customer use cases.

• Proven expertise in Dev Ops and LLMOps practices.
• Knowledgeable in Kubernetes, Docker, and container orchestration.
• Deep understanding of LLM orchestration frameworks like Flowise, Langflow, and Langgraph.

Databases/Data warehouse:
Dynamo

DB, Cosmos, Mongo

DB, RDS, MySQL, PostGreSQL, Aurora, Spanner, Google Big Query.

Cloud Certifications (Bonus): AWS Professional Solution Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert