Jobs at Oteemo Inc.

Designs scalable data architectures and pipelines supporting digital twins, analytics, and operational modeling. Develops a digital twin of operating-room environments, translates workflows into data and simulation models, and builds scenario-analysis capabilities for staffing, scheduling, capacity, patient flow, and resource utilization. Creates dashboards, APIs, and decision-support tools while establishing data governance, quality, lineage, validation, documentation, and monitoring. Collaborates with healthcare stakeholders, technical teams, and government leadership.

Build and deploy AI-powered applications, LLM agents, RAG pipelines, backend services, APIs, and frontend applications. Integrate cloud AI services, databases, Docker, and infrastructure-as-code while applying secure development practices. Collaborate with engineering, business, and client teams to prototype solutions, conduct workshops, and deliver client-facing features. The role may evolve into forward-deployed engineering with direct client engagement.

Design and deploy optimization and machine learning models for allocating scarce parts and resources across competing programs. Responsibilities include formulating MILP and constraint optimization models, forecasting demand and supply risk, building data pipelines, validating outputs, communicating tradeoffs to stakeholders, mentoring junior data scientists, and partnering with software engineers to productionize scalable services.

Designs and implements large-scale cloud and hybrid infrastructure solutions while supporting customer development, testing, and production environments. Leads a small Cloud Engineering team, administers Azure and Kubernetes platforms, networking, Linux systems, GitLab CI/CD pipelines, security tooling, and infrastructure-as-code. Builds testing infrastructure and container or machine images, performs vulnerability scanning, and manages upgrades and maintenance. Communicates technical designs to executives and nontechnical stakeholders.

Develop and maintain C/C++ applications on Linux, using command-line toolchains and CI/CD pipelines to compile, test, containerize, and deliver software artifacts. Apply Git, code reviews, static analysis, troubleshooting, design patterns, architectural principles, and SOLID practices to build scalable systems. Collaborate with development, operations, security, and business teams to ensure reliable, secure software delivery.

Administer and secure Microsoft Azure environments supporting development, testing, and production deployments. Manage networking, Linux systems, GitLab CI/CD pipelines, vulnerability scanning, security tooling, Kubernetes clusters, infrastructure images, and ongoing platform upgrades. Use Infrastructure as Code and Bash or Python automation while providing technical leadership and consulting support for customer systems.

Lead the design, development, and operation of secure, scalable distributed APIs and data services in cloud environments. Responsibilities include architecting GraphQL and REST services, developing TypeScript and Go applications, managing serverless and containerized workloads, implementing infrastructure as code and CI/CD, designing data and messaging workflows, establishing observability and resiliency patterns, and ensuring security and compliance. The role also mentors engineers and collaborates with product, compliance, and security teams.

Design and implement end-to-end AI features, scalable data pipelines, ETL/ELT workflows, Generative AI and agentic systems, ML and BI solutions, and MLOps/LLMOps practices across AWS, Azure, and GCP. Apply RAG, embeddings, vector databases, security governance, evaluation, reliability, and guardrails. Own technical workstreams, mentor junior engineers, and promote software engineering best practices in consulting environments.

Designs, builds, and maintains internal developer platform self-service tooling and infrastructure automation. Responsibilities include authoring Terraform and AWS CDK infrastructure, provisioning AWS EKS environments, integrating Backstage, Cookiecutter, JFrog, GitHub Actions, and Argo Workflows, and building secure, production-grade CI/CD pipelines and developer workflows.

Build and scale production enterprise AI applications using RAG, LLM integrations, agents, vector search, and prompt evaluation. Develop Python/FastAPI backends, React/Next.js frontends, real-time services, and database architectures. Deploy AI workloads with Docker, Kubernetes, CI/CD, cloud infrastructure, and Terraform. Implement monitoring, testing, security, observability, and cost optimization for reliable, scalable AI systems.