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Work Background
HyperFounder
Mar. 2025RemoteAt Hyper, we’re architecting a next-generation agentic infrastructure layer optimized for autonomy, persistence, and sovereign deployment. Our thesis: LLMs are stateless by design—Hyper introduces persistent memory, system-level reflection, and retrieval-augmented reasoning directly into the agent execution loop. • Designed and implemented a memory architecture with vectorized embeddings, relevance-ranked retrievers, and temporal decay models to support long-term agent continuity. • Developed a real-time context injection layer compatible with OpenAI, Claude, and open-source LLMs (e.g. Mistral, Phi-3, local fine-tunes). • Built a memory-first agent runtime capable of goal decomposition, tool selection, reflection loops, scratchpad planning, and execution scoring. • Engineered agent-to-agent communication protocols with memory-passing, dynamic role assignment, and multi-agent orchestration. • Delivered developer tooling including declarative agent schema configs, CLI simulators, memory persistence APIs, and live evaluation pipelines. • Architected the full system to support modular, offline-compatible, privacy-first deployments within airgapped and containerized environments. • Hyper functions as the cognitive substrate for autonomous agents—providing long-horizon memory, reflection scaffolding, and real-time execution infrastructure. This work sits at the intersection of LLM orchestration, systems design, and the emergence of durable, reasoning-capable AI agents.
Defense UnicornsPlatform Engineer
Apr. 2023 - Jan. 2025United States• Designed, deployed, and optimized Kubernetes clusters using RKE2, EKS, and AKS. Built scalable, production ready workloads with advanced networking, RBAC, and security policies.
• Automated infrastructure and application deployments using ArgoCD, Flux, Helm, and customized CI/CD pipelines. Developed robust Helm charts to standardize deployments across environments.
• Built and maintained secure, isolated environments using Zarf Dev, ensuring compliance with DoD and other government security requirements.
• Designed and implemented DevSecOps workflows integrating Kubernetes, Terraform, Ansible, and containerized applications with high-efficiency CI/CD pipelines.
• Developed ML infrastructure for training and deploying LLMs to production environments
• Implemented distributed training pipelines that reduced model training time by 42%
• Built model optimization tools for efficient inferencing, reducing latency by 75%
• Created vector database solutions for semantic search and retrieval applications
• Developed automated ML evaluation frameworks to ensure model quality and performance
• Implemented ML observability tools for monitoring model drift and performance
OPSWATSRE / DevOps Engineer (ML Engineering Responsibilities)
Jun. 2021 - Mar. 2023United States• Developed highly concurrent backend services in Go and Python handling 50K+ requests per second with 99.99% uptime
• Built real-time data processing system using Kafka and Redis that decreased event processing latency from seconds to milliseconds
• Optimized database queries and implemented caching strategies that reduced database load by 60%
• Created CLI tooling for internal development workflows that increased engineer productivity by 25%
• Implemented service mesh architecture with Istio, improving system reliability and observability
MotionalDevOps Engineer
Oct. 2018 - May. 2021United StatesResults-driven DevOps Engineer with deep expertise in designing, automating, and
optimizing cloud-native infrastructure. Proven success in implementing scalable CI/CD
pipelines, cloud migrations, and microservices architecture. • Led infrastructure transitions from on-premises to AWS, ensuring optimized security, scalability, and cost efficiency. Proficient in hybrid-cloud environments, maintaining seamless integration across cloud and on-prem systems.
• Designed and implemented CI/CD pipelines across SDLCs,
automating deployment processes within Kubernetes and Docker environments.
Developed Jenkins-based release automation tools and leveraged Terraform, Ansible,
and CloudFormation for infrastructure as code.
• Architected and maintained containerized applications using Kubernetes, Docker, and Helm. Optimized deployment workflows within AWS, including cross-region deployments and auto-scaling strategies.
• Hands-on expertise across AWS services, including IAM, VPC, Route 53, EKS, EC2, S3, CodeBuild, CodeDeploy, Redshift, RDS, CloudWatch, and Lambda. Developed automated cloud infrastructure provisioning using Terraform and CloudFormation.
• Automated cloud operations, monitoring, and security
workflows using Python, Bash, and JavaScript. Experience with Conan package
management, Makefiles, CMake, and Ninja for efficient build automation.
• Integrated static analysis tools such as SonarQube and
Klocwork within microservices architecture. Automated security compliance and
infrastructure monitoring using CloudWatch, Prometheus, and Grafana.
• Partnered with software teams to assess
DevOps needs, ensuring seamless infrastructure implementation and deployment.
Automated and optimized Atlassian suite tools (Jira, Confluence, Bitbucket) for
streamlined project management and DevOps workflows.
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