Jobs at OneByZero

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.

Design, build, and optimize end-to-end data ingestion and ETL pipelines on AWS into governed data lake zones. Implement data quality frameworks, error handling, and metadata/catalog management. Use IaC (Terraform/AWS CDK), write tests, support UAT, produce runbooks/documentation, and participate in Agile delivery.

Lead backend design and implementation for scalable AWS-based APIs, own CI/CD and secure deployments, act as client-facing technical lead, mentor engineers, and deliver large, reliable modules (50% leadership, 50% individual contribution).

Owner of automated testing for the NEO platform: design test strategy, build and maintain API/integration, E2E UI, regression, and load tests (k6). Integrate suites into CI/CD (GitHub Actions), manage flaky tests, and collaborate with engineering to ensure release quality.

As a Senior DevOps Engineer, you will enhance our AWS infrastructure and deployment processes, automate tasks, manage CI/CD pipelines, and mentor junior engineers.

The Artificial Intelligence Engineer at OneByZero builds production-grade agentic systems on AWS, integrating into enterprise realities, and ensuring compliance and performance evaluation throughout the process.

Design, provision, and secure AWS cloud environments for large-scale data platforms. Implement networking, Lake Formation, CI/CD, cross-account connectivity, monitoring, governance, RBAC/TBAC, encryption, and automated IaC. Produce architecture documentation, runbooks, and collaborate in Agile squads supporting regulated, classified environments.

As Engagement Manager, you will manage client accounts, ensuring profitability and successful delivery of AI solutions through strategic relationships and governance frameworks. Responsibilities include driving business growth, managing P&L, and coaching teams to deliver impactful results.

The Engagement Manager will lead client accounts, drive business growth, oversee delivery, manage P&L, and develop high-performing teams.