Agentic AI-Sr Architect

Job Title: Agentic AI-Sr Architect 

Location: Santa Clara, CA

Duration: Full time

Education:

·       Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field

·       Advanced coursework or certifications in machine learning, deep learning, or NLP

·       Strong mathematical and statistical foundation

Experience Range

·       6–10 years of experience in AI/ML/Deep Learning model development

·       2-3 years Hands-on experience with LLMs, NLP, or speech/voice AI systems

·       3-5 years of experience deploying AI solutions in production environments

Primary (Must have skills)

·       5+ years of experience in Python, PyTorch, TensorFlow, or similar frameworks

·       3-5 years of experience designing, training, and fine-tuning large language models or AI models, speech/voice AI systems, Realtime voice pipeline

·       5 years of experience in cloud AI platforms (AWS Sagemaker, Azure ML, Google Cloud Platform AI)

·       2+ years of experience in Agentic AI frameworks such as LangChain, LangGraph, A2A, MCP, and Multi agent orchestration

·       2-3 years of experience Familiarity with model evaluation metrics, bias detection, and optimization

·       5 years of experience in  integrating AI models into applications via APIs or pipelines

·       2-3 years experience in Azure services — Azure AI Speech and Translator, Azure OpenAI, AI Search, plus Container Apps/AKS, API Management, Event Hubs, Key Vault, and Application Insights

Key Technical Skills :

·       Design, develop, and deploy advanced AI models to meet business requirements

·       Collaborate with data engineers, LLM Ops, and software teams for end-to-end solutions

·       Evaluate model performance, fine-tune, and ensure scalability and reliability

·       Research emerging AI/ML technologies and propose innovative solutions

·       Mentor junior AI engineers and review code/models for best practices

Secondary Skills

·       Advanced LLM techniques, prompt engineering, and fine-tuning strategies

·       AI ethics, fairness, and responsible AI deployment

·       Continuous learning on emerging AI frameworks and architectures

·       LLMOps - layered evals (WER, entity F1, COMET), CI regression gates, tracing, cost-per-minute telemetry, model routing, and drift detection.