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Design and build production-grade generative AI systems, including LLM architectures, fine-tuning pipelines, evaluations, RAG, and multi-agent integrations. Optimize models for latency, throughput, token efficiency, cost, and reliability. Deploy secure AWS-native solutions, implement monitoring and guardrails, translate client requirements into model strategies, and contribute production code, design reviews, reusable patterns, and technical knowledge sharing.

Design and build production-grade generative AI systems, including fine-tuned LLMs, RAG pipelines, and multi-agent architectures. Develop evaluation, monitoring, safety, and observability pipelines; optimize model latency, throughput, token usage, and cost; and deploy secure enterprise solutions on AWS. Translate client requirements into model strategies, contribute to architecture and integration patterns, review code and designs, and deliver reusable engineering assets for regulated-industry engagements.

Designs and builds production-grade generative AI systems, including LLM architectures, fine-tuning pipelines, evaluations, RAG and multi-agent integrations. Optimizes models for latency, throughput, token efficiency, reliability, and cost. The role also develops enterprise integration, governance, monitoring, safety, and deployment patterns on AWS, while translating client requirements into model strategies and contributing hands-on production code, design reviews, and reusable engineering practices.

Design and build production-grade generative AI systems, including LLM architectures, fine-tuning pipelines, model evaluations, RAG systems, and multi-agent integrations. Optimize models for latency, throughput, token efficiency, and cost while implementing monitoring, guardrails, rollback, and enterprise security patterns. Translate client requirements into model strategies, contribute production code and design reviews, and collaborate across engineering, data, product, and client teams.

Design and build production-grade generative AI systems, including LLM architectures, fine-tuning workflows, evaluation pipelines, RAG systems, and multi-agent integrations. Optimize models for latency, throughput, token efficiency, reliability, and cost. Deploy secure AWS-native solutions, establish monitoring and governance patterns, translate client requirements into model strategies, and contribute production-quality code, design reviews, reusable engineering practices, and client-facing delivery.

Design and build production-grade generative AI systems, including LLM architectures, fine-tuning pipelines, evaluations, RAG systems, and multi-agent integrations. Optimize models for latency, throughput, token efficiency, reliability, and cost. Deploy secure enterprise solutions on AWS, implement monitoring and guardrails, translate client requirements into model strategies, and contribute hands-on production code, design reviews, and reusable engineering practices.

Design and build production-grade generative AI systems, including LLM architectures, fine-tuning pipelines, RAG systems, model evaluations, and multi-agent integrations. Optimize models for latency, throughput, token efficiency, reliability, and cost. Deploy secure enterprise solutions on AWS, implement monitoring and guardrails, translate client requirements into model strategies, and contribute production-quality code, design reviews, and reusable engineering practices.

Own the design and production hardening of a low-latency, highly concurrent voice pipeline. Build WebRTC, WebSocket, gRPC, RTP, CTI, and telephony integrations; architect scalable AWS session infrastructure; optimize STT/TTS latency; and implement VAD, endpointing, jitter buffering, packet-loss handling, and barge-in. The role supports thousands of concurrent enterprise sessions and requires end-to-end ownership from architecture through production deployment.

Lead data and AI strategy initiatives, develop client solutions, translate business needs into actionable roadmaps, and drive measurable business transformation outcomes.

Design, build, evaluate, and operate production agentic AI systems on AWS and the OBZ Neo platform for regulated enterprises. Implement multi-agent orchestration, RAG pipelines, tooling, MCP servers, human-in-the-loop workflows, and integrations. Define evaluation metrics, practice DevSecOps, ensure security/compliance, and deliver reusable IP and runbooks while collaborating with client teams.