Jobs at Pragmatike

Build Pragmatike's outbound sales engine: define messaging, prospect, and generate meetings with startup founders, CTOs, and hiring leaders across Europe and North America. Work directly with founders to iterate playbooks, manage your own pipeline, and grow the business in a remote, high-ownership startup environment.

Serve as primary HR partner for APAC, supporting full employee lifecycle, compliance with local labor laws, HRIS and payroll coordination, recruitment, performance cycles, employee relations, reporting HR metrics, and regional engagement initiatives.

Lead and grow a small engineering team building and operating an end-to-end AI product: model serving, inference infrastructure, GPU capacity, deployment/versioning, retrieval and embedding pipelines, monitoring, and quality evaluation. Balance hands-on engineering (~50%) with architecture, cross-functional collaboration, and technical leadership to deliver reliable, cost-efficient AI systems.

Lead a small engineering team building and operating the end-to-end AI product platform: model serving, GPU scheduling, inference pipelines, retrieval/embedding systems, monitoring, and CI/CD. Balance hands-on coding (~50%) with technical leadership, architecture, cross-functional collaboration, hiring and coaching, and driving reliability, cost-efficiency, and AI product quality.

Lead a small AI engineering team (3 engineers) building and operating the full AI product stack: model serving, inference pipelines, GPU scheduling, deployment/versioning, retrieval and embedding pipelines, monitoring, evaluation, and integrations. Balance hands-on coding (~50%) with technical leadership, architecture decisions, cross-team collaboration, hiring and coaching, and delivery planning to ensure reliable, cost-efficient AI systems at scale.

Lead a small engineering team owning the AI product stack: model serving, inference pipelines, GPU scheduling, deployment/versioning, and observability. Provide hands-on engineering (~50%), set architecture, drive cost/performance improvements, coordinate cross-functionally, mentor engineers, and ensure reliable, scalable AI platform delivery.

Build and operate production-grade model serving infrastructure, design deployment pipelines (blue/green, canary), implement autoscaling and multi-model serving, optimize GPU utilization and network throughput, set up observability and model registries, manage CI/CD for reproducible deployments, own full ML system lifecycle including on-call support and platform scalability.

Drive outbound prospecting to generate new sales opportunities for early-stage startups. Build outbound strategy, messaging, and workflows; engage founders, CTOs, and hiring leaders via LinkedIn, email, and cold outreach; manage your pipeline and work closely with founders to convert prospects.

The Principal AI/ML Engineer will lead the design, implementation, and deployment of AI systems, mentor junior engineers, and ensure model performance and reliability in production environments.

Build and maintain production-grade React+TypeScript web interfaces for data-intensive, real-time AI products. Own end-to-end frontend features, optimize UI performance for large datasets, collaborate with design and backend teams, establish frontend best practices, improve architecture and tooling, and resolve production issues across browsers and devices.

The Principal ML Ops Engineer will design, implement, and scale ML infrastructure, focusing on optimizing AI systems, collaborating with teams, and establishing best practices for ML Ops.

Owner/implementer of end-to-end full stack features for data-intensive AI products: design and build scalable Node.js backends, REST APIs, and React+TypeScript frontends; optimize performance; establish testing, observability, and CI/CD; collaborate with product, design, and ML teams; debug and support production systems.

You will design, implement, and optimize NVIDIA CUDA kernels for AI systems, improve GPU memory pipelines, and profile workloads to enhance performance.

The AI Infrastructure Engineer will build and manage scalable model serving infrastructure for AI/ML applications, ensuring high efficiency and reliability in production.

Design, build, and maintain scalable backend services and APIs using TypeScript and NestJS. Implement event-driven, high-throughput workflows, integrations, queues/workers, and observability; collaborate across teams and own projects from design to deployment.

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

Design, build, and maintain scalable backend services and distributed systems using NestJS. Architect APIs, caching, queues, and third-party integrations. Collaborate with frontend and product teams, improve performance and reliability, leverage AI-assisted development workflows, and own features end-to-end in a fast-moving, remote-first SaaS/MedTech environment.

Build and maintain high-throughput backend services in native PHP (7/8). Design and optimize transaction-heavy billing workflows, REST/SOAP APIs, relational schemas, async processing (RabbitMQ/Kafka), caching (Redis/Memcached), and integrations with external payment/carrier systems. Ensure security, observability, CI/CD, and collaborate with product, ops and QA to run and improve production services.

Design, build and maintain high-throughput backend services in native PHP (7/8). Implement transactional billing workflows, REST/SOAP integrations, database schema and query tuning, async processing with queues and cache, ensure security and observability, and contribute to CI/CD and production operations.

Develop and maintain native PHP (7/8) backend services for high-volume transaction and billing workflows. Build REST/SOAP APIs, optimize relational databases, integrate external gateways, implement async processing with queues and caching, ensure security and observability, and contribute to CI/CD and production reliability.