AI Engineer

 Platform Development and Evangelism:

• 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.

·  Machine Learning Pipeline Design:

• 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, SageMaker, Vertex AI, and Azure AI.

LLM Serving and GPU Architecture:

• 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 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.

LLM Models and Use Cases:

• 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.

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.

Communication & Articulation

• Ability to explain complex AI/ML topics and design choices to technical and business audiences.
• Experience in presenting AI strategies and results to senior executives, highlighting impact.
• Ability to lead cross-functional discussions to clarify issues and achieve engineering consensus.
• Ability to persuade stakeholders and secures support on solution approaches.

Continuous Innovation & Adaptive Learning

• Proactively tracks emerging AI research, frameworks, and industry design patterns.
• Validates new concepts through quick experimentation and iterative "fail-fast" testing.
• Translates cutting-edge developments into practical improvements for production systems.
  • Demonstrates a self-driven commitment to learning and adopting evolving AI technologies.