AI Architect - Onsite - F2F Interview

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Job Title: AI Architect

Onsite interview. Richardson, TX

Summary

Need someone who can start ASAP, Inperson interview

The ideal candidate is a hands-on AI Architect with deep expertise in LLM-based systems, agentic architectures, and AWS-native platforms, combined with strong experience in guardrails, evaluation frameworks, and cost optimization. This role requires a balance of advanced technical depth and architectural leadership to deliver scalable, production-grade AI platforms

Enterprise AI & LLM Architecture (8 years total, 4 years in AI/ML architecture)

Architect and deliver enterprise-scale AI systems leveraging Large Language Models (LLMs), including Retrieval-Augmented Generation (RAG), fine-tuning strategies, and advanced agentic architectures.

Design multi-agent systems capable of tool orchestration, planning, reasoning, and context management for complex, distributed workflows.

LLM Guardrails & Responsible AI (1 years)

Design and implement guardrails for LLM applications, including content filtering, prompt injection mitigation, hallucination reduction, and policy enforcement.

Experience with safety frameworks, human-in-the-loop validation, and governance controls to ensure compliance, reliability, and ethical AI usage.

LLM Cost Optimization & Performance Engineering (1 years)

Optimize LLM workloads for cost, latency, and throughput, including model selection strategies, prompt engineering efficiency, caching mechanisms, batching, and token optimization.

Experience implementing cost monitoring, usage controls, and scalable inference architectures to balance performance with operational efficiency.

Evaluation Frameworks & Model Quality (Evals) (1 years)

Develop and operationalize evaluation (evals) frameworks for LLMs, including automated benchmarking, prompt evaluation, response scoring, and regression testing.

Experience with both offline and online evaluation techniques, including human evaluation pipelines, A/B testing, and continuous feedback loops to improve model quality and reliability.

AWS Bedrock & Agentic Frameworks (2 years hands-on)

Hands-on experience with Amazon Bedrock, including foundation model evaluation, prompt engineering, orchestration, and model lifecycle management.

Experience with AgentCore or similar agent frameworks for building composable, stateful, and tool-augmented AI agents with memory and execution control.

Advanced Python & API Engineering (6 years)

Expert-level proficiency in Python with deep experience across AI/ML ecosystems (PyTorch, TensorFlow, Hugging Face, LangChain, or equivalent).

Strong experience building scalable, production-grade APIs using FastAPI, including async processing, model inference optimization, and microservices architecture.

CI/CD and MLOps for AI Systems (5 years)

Design and implement robust CI/CD pipelines for AI applications, including automated model testing, validation, reproducibility, and staged rollouts.

Experience with MLOps frameworks, model versioning, feature stores, and continuous monitoring using tools such as GitHub Actions, Jenkins, AWS CodePipeline, and Terraform/CloudFormation.

AWS Cloud Architecture (2 years)

• Basic expertise in Amazon VPC design, including subnetting strategies, private networking, security segmentation, and high-availability architecture for AI workloads.
• Hands-on experience with AWS Lambda and event-driven patterns for scalable, serverless inference pipelines and integration with AWS services (API Gateway, SQS, Step Functions).
• Distributed Team Leadership & Global Delivery (5 years)
• Proven experience leading distributed engineering and AI development teams across multiple time zones, ensuring high productivity and alignment.
• Strong track record of implementing agile delivery models, managing cross-regional collaboration, and driving continuous delivery across globally dispersed teams.