Senior ML Architect

• Design end -to -end ML architectures for Velogent AI's framework, enabling governed multi -agent systems in financial services, healthcare, and life sciences
  
• Build scalable inference infrastructure leveraging for sub -second latency throughput
  
• Create MLOps pipelines integrating Kubeflow, MLflow, Ray, and Karpenter for distributed training and intelligent autoscaling on Amazon EKS
  

Accelerator Innovation & Client Delivery

• Lead technical design of AI accelerator features—from distributed compute orchestration to multi -tenant isolation and disaster recovery patterns
  
• Architect Convogent AI voice systems with real -time STT/TTS, contextual memory, and post -call analytics serving 250+ concurrent sessions
  
• Translate ambiguous business challenges into concrete ML solutions—from autonomous invoice processing to conversational intelligence
  

Technical Leadership & Evangelism

• Partner with customers and cloud teams on enterprise AI engagements, conducting architecture reviews and POCs for Fortune 500 clients
  
• Define best practices for LLM serving, prompt engineering, guardrails, and model evaluation across accelerator products
  
• Mentor ML engineers on agentic design patterns, production ML, and cloud -native AI infrastructure
  
• Contribute to internal R&D—experimenting with frontier models, emerging frameworks (MCPs, agent orchestration), and optimization techniques
  

What You Bring

Core Expertise

• 8+ years building production ML systems, with 3+ years architecting at scale (distributed training, high -throughput inference, or MLOps platforms)
  
• Deep hands -on experience with ML workloads— GPU scheduling, autoscaling (Karpenter), distributed frameworks (Ray, Kubeflow)
  
• Strong foundation in LLM architectures and Generative AI—prompt engineering, RAG pipelines, model serving (vLLM, Triton, SageMaker), fine -tuning, and evaluation
  
• Proficiency in MLOps tooling—experiment tracking (MLflow/Weights & Biases), feature stores, model registries, CI/CD for ML, monitoring frameworks
  

Technical Depth

• Solid software engineering skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, HuggingFace)
  
• Experience with cloud -native architectures on AWS—SageMaker, Bedrock, EKS, Lambda, managed databases, networking, IAM
  
• Understanding of agentic AI patterns—multi -agent orchestration, tool use (function calling), reasoning frameworks, validation layers
  
• Knowledge of voice AI technologies (STT/TTS, WebRTC, real -time streaming) or governance frameworks for regulated AI is a strong plus
  

What Makes You Special

• Builder mindset—you prototype fast, iterate based on feedback, and obsess over production reliability
  
• Business acumen—you translate technical decisions into ROI narratives that resonate with CTOs and business leaders